#Longitudinal trend analysis

[marketxcel]

What Is Trend Analysis in Research? Types, Methods, and Examples

Explore the essence of trend analysis in research, encompassing its diverse types, methodologies, and real-world examples. Unravel the significance of tracking trends to glean insights and make informed decisions in various fields.

[evidence-based-activism]

could you elaborate on often brought up statistics that men commit suicide more often?

Sure!

Prevalence Differences

So, as you've stated, men are more likely to die by suicide. According to the CDC, in the USA, men account for about 80% of suicides [1]. This narrows to about 60% of suicides worldwide [2].

This review article [2] notes that "one of the most consistent findings in suicide research is that women make more suicide attempts than men, but men are more likely to die in their attempts than women."

Much of this gap can be explained by differences in suicide methodology: men tend to use more lethal methods such as firearms [3, 4]. Despite claims to the contrary, however, this difference does reflect the "lethality of their suicidal intent". In other words, women and men who attempt suicide are similarly "intent on dying", but men's choice of methodology means they are more likely to die by suicide.

Crime and suicide

I've seen some commentary questioning the degree to which men's abusive/criminal behavior underlies their greater suicide rates, so I thought I would try to answer this question. (To the degree that it can be answered.)

First, I should be clear that there are many, many interconnected risk factors for suicide [5, 6] and any individual suicide is likely to be multi-factorial. That being said, there is a connection between men's violent and suicidal behavior, which is too often neglected in this conversation.

Criminality in general:

This representative analysis of male suicides in the USA found arrest accounted for ~9% of male suicides. Notably, when dis-aggregated by race, arrest accounted for a statistically significant proportion of White male and Hispanic male suicides, but not Black male suicides. This study did not evaluate the impact of violent vs non-violent crime or conviction. [7]

This representative, longitudinal Swedish study found "violent offenders had nearly five times higher risk ... to die from suicide and non-violent criminals had about two times higher risk." [8]

This representative longitudinal Danish study found "more than a third of all male suicides (34.8%) had a criminal justice history" and that men convicted of sexual or violent offenses had between a 3 and 5-fold higher risk of suicide, and these offenses represented ~7% of all male suicides in the sample. [9]

Intimate partner violence, specifically:

This study found intimate partner violence accounts for over 10% of violent deaths. Of this, 43% were either homicide-suicides, single-suicides (by male perpetrators), or "suicide by police", where each category was majority-male. [10]

This study analyzes the data in another way, finding that "intimate partner violence is a precipitating factor for 4.5% of single suicides", most of which "were of men who perpetrated nonfatal IPV". They also conclude that "when combined with homicide-suicide data, IPV influences 6.1% of suicides overall". (Based on their data tables, this means male IPV perpetration preceded ~7% of all male suicides.) [11]

This study found that, "among men who killed their female intimate partner with a firearm, 59% also took their own life". (They were also not more likely to be on antidepressants than other male suicide victims.) [12]

This study did not directly examine intimate partner abuse, but did find "most male suicide decedents had no known mental health conditions" and one major risk factor was "arguments" with a romantic partner. Considering this effect was strongest for "suicides that occurred during the argument itself" this is likely at least partially driven by the same trends as above. [13]

While the limitations in national data mean the results cannot be definitively confirmed, the trends appear to be similar across the USA. [14]

This Australian report found at least 23% of male suicides involved "family violence", with the decedent male as the perpetrator >80% of the time. [15]

Clearly, there is a connection between men's abusive behaviors and their suicide. It's not the only factor, but it is a significant one, even more than criminality or violent behavior in general.

---

In conclusion, women are more likely to attempt suicide, but men are more likely to die by suicide. Men's reasons for suicide are multifactorial, but there is an undeniable connection with their abuse of women.

I hope this helps you, Anon! Let me know if I didn't address a specific question you had!

References under the cut:

CDC. “Suicide Data and Statistics.” Suicide Prevention, 29 Oct. 2024, https://www.cdc.gov/suicide/facts/data.html.

Vijayakumar, L. (2015). Suicide in women. Indian journal of psychiatry, 57(Suppl 2), S233-S238.

Denning, D. G., Conwell, Y., King, D., & Cox, C. (2000). Method choice, intent, and gender in completed suicide. Suicide and Life‐Threatening Behavior, 30(3), 282-288.

Bommersbach, T. J., Rosenheck, R. A., Petrakis, I. L., & Rhee, T. G. (2022). Why are women more likely to attempt suicide than men? Analysis of lifetime suicide attempts among US adults in a nationally representative sample. Journal of affective disorders, 311, 157-164.

Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., ... & Nock, M. K. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological bulletin, 143(2), 187.

Richardson, C., Robb, K. A., & O'Connor, R. C. (2021). A systematic review of suicidal behaviour in men: A narrative synthesis of risk factors. Social Science & Medicine, 276, 113831.

Bryson, W. C., Piel, J., & Thielke, S. M. (2021). Arrest and non-fatal suicide attempts among men: analysis of survey data from the National Survey on Drug Use and Health. BMC psychiatry, 21, 1-8.

Stenbacka, M., Romelsjö, A., & Jokinen, J. (2014). Criminality and suicide: a longitudinal Swedish cohort study. BMJ open, 4(2), e003497.

Webb, R. T., Qin, P., Stevens, H., Mortensen, P. B., Appleby, L., & Shaw, J. (2011). National study of suicide in all people with a criminal justice history. Archives of general psychiatry, 68(6), 591-599.

Kafka, J. M., Moracco, K. E., Young, B. R., Taheri, C., Graham, L. M., Macy, R. J., & Proescholdbell, S. K. (2021). Fatalities related to intimate partner violence: towards a comprehensive perspective. Injury prevention, 27(2), 137-144.

Kafka, J. M., Moracco, K. B. E., Taheri, C., Young, B. R., Graham, L. M., Macy, R. J., & Proescholdbell, S. (2022). Intimate partner violence victimization and perpetration as precursors to suicide. SSM-Population Health, 18, 101079.

Barber, C. W., Azrael, D., Hemenway, D., Olson, L. M., Nie, C., Schaechter, J., & Walsh, S. (2008). Suicides and suicide attempts following homicide: victim–suspect relationship, weapon type, and presence of antidepressants. Homicide studies, 12(3), 285-297.

Fowler, K. A., Kaplan, M. S., Stone, D. M., Zhou, H., Stevens, M. R., & Simon, T. R. (2022). Suicide among males across the lifespan: An analysis of differences by known mental health status. American journal of preventive medicine, 63(3), 419-422.

Kafka, J. M., Moracco, K. E., Pence, B. W., Trangenstein, P. J., Fliss, M. D., & Reyes, L. M. (2024). Intimate partner violence and suicide mortality: a cross-sectional study using machine learning and natural language processing of suicide data from 43 states. Injury prevention, 30(2), 125-131.

Coroners Court of Victoria. (2024). Experience of family violence among people who suicided 2009-2016. https://www.coronerscourt.vic.gov.au/sites/default/files/2024-09/Coroners%20Court%20of%20Victoria%20Experience%20of%20family%20violence%20among%20people%20who%20suicided%202009-20016.pdf

[bwhitex]

Alive = wild x Nature

Alive

If feeling alive is more than just being alive, physically. We need to keep living. It’s a madness to fully feel alive but genius because it’s also the intelligent thing to feel often. Our recovery is found in fully feeling "Alive". In this equation Alive is a state that encompasses more than just biological function; it involves a holistic engagement with the environment, society, and one's intrinsic nature. To be alive is not only to breathe and function but also to interact dynamically with the complex systems around us, including technology, social structures, family, and work. This state is constantly influenced by the equilibrium between our internal wildness and the external order imposed by nature. The hypothesis "Alive = wild X Nature" suggests that being truly alive is a product of both our innate wild characteristics and our relationship with the natural world. This hypothesis can be tested by examining how individuals who are more connected to their primal instincts and to nature report a greater sense of vitality and well-being. One could measure this through psychological assessments that gauge feelings of aliveness and energy. Additionally, lifestyle factors, such as time spent outdoors and in natural settings, could be quantified to establish a correlation with the individuals' sense of being alive. If the hypothesis holds true, we would expect to see a positive relationship between these factors and the reported experience of feeling alive.

Wild

The wild component of the equation represents the untamed, instinctual part of our existence. It is the part of us that operates outside the structured confines of modern society and technology. To explore if and how this 'wildness' contributes to the state of being alive, one could examine lifestyles that incorporate elements of the wild, such as adventure sports, wilderness exploration, or even urban environments that offer a degree of unpredictability and require instinctual responses. The hypothesis suggests that the more one engages with these wild aspects, the more 'alive' one might feel. To test this, one could compare individuals with different levels of exposure to wild environments and assess their psychological and physiological markers of aliveness. The presence of a stronger sense of vitality and well-being in those more connected to wild aspects would support the hypothesis. Moreover, an analysis of historical figures and populations who have lived closer to 'wild' conditions could provide anecdotal evidence for the importance of this component in the experience of being alive. If true, this would imply that modern life, which often distances us from our wild roots, might require deliberate integration of wild experiences to fulfill the equation of being alive.

Nature

Nature, as a concept within the equation, represents the external, environmental aspect of being alive that interacts with our internal wildness. The hypothesis implies that a deep connection with nature, through environmental stewardship, outdoor activities, or simply time spent in natural settings, is essential for feeling alive. One can test this by examining the psychological impact of nature on individuals through nature therapy or ecotherapy sessions. Studies have shown that exposure to nature can reduce stress, enhance mood, and improve cognitive function, suggesting a link between nature and the sensation of being alive. Longitudinal studies that track participants' well-being over time as they increase their interaction with nature provide further evidence supporting the hypothesis. Additionally, cross-cultural studies have also examined the role of nature in various societies and the corresponding levels of reported aliveness. Because “nature” is indeed a critical component of being alive, as the hypothesis suggests, populations with greater access to and engagement with natural environments do report higher levels of vitality. The current trends in urbanization and environmental degradation are often pose a significant threat to the collective sense of aliveness, underscoring the need for sustainable and nature-integrated urban planning.

Integration with Technology (`T`)

Technology presents a complex interplay with the concept of being alive. On one hand, it can enhance our capabilities and improve our chances of survival, thereby augmenting the 'alive' experience. On the other, it may distance us from our wild roots and natural environments. To test the hypothesis in the context of technology, one could examine how the use of technology affects our sense of connection with the wild and nature. For instance, does the use of fitness trackers and health apps improve our health and vitality, or do they create a disconnect by mediating our experience of the natural world? Studies might involve contrasting the well-being of individuals who use technology to augment their natural experiences with those who feel overwhelmed by it. Furthermore, we wi explore whether societies with higher levels of technological integration report a different quality of aliveness than less technologically advanced societies. If the hypothesis is correct, then technology should serve as a tool that, when used appropriately, can enhance our wild and natural experiences rather than replace them.

Integration with Society (`S`)

Society has its own set of rules and expectations that can both nurture and constrain the wild aspect of being alive. To test the role of society in the equation "Alive = wild X Nature," we can look at different social structures and their impact on individuals' connection to their innate wildness and the natural world. Societies with rigid structures may suppress wild tendencies, potentially affecting the sense of being alive negatively. In contrast, societies that celebrate individuality and are closer to nature because it enhances this feeling. One needs only to examine cross-societal studies that measure levels of reported vitality and aliveness inconjunction with societal norms regarding wildness and nature, to see that though. Such studies do control for other variables like economic status and access to technology to isolate the effect of societal integration. The hypothesis predicts that societies that foster a balance between the natural world and individual expression of wildness would have populations reporting a higher sense of aliveness. Additionally, by investigating historical societies that had varying degrees of wildness integration could provide insights into the role of societal structure in the experience of being alive. If the hypothesis holds true, there should be a discernible pattern indicating that societies with a closer relationship to nature and a tolerance for wildness contribute positively to their members' sense of aliveness.

Integration with Family Dynamics (`F`)

Family dynamics play a pivotal role in shaping how we experience being alive, especially in relation to our inherent wildness and connection with nature. The hypothesis "Alive = wild X Nature" implies that family influences, such as parenting styles and familial relationships, can either suppress or encourage the wild aspects of our nature. To test this, evualting and comparing individuals raised in families with varying degrees of openness to wildness and nature. Studies show families that embrace outdoor activities, risk-taking, and freedom may develop a stronger sense of being alive compared to those from more controlled and nature-averse family environments. Research that involved longitudinal studies tracking individuals from different family backgrounds and their reported levels of vitality and connection to nature. Moreover, the role of family narratives and histories related to nature and the wild could be examined to see how they impact the individual's perception of being alive. If the hypothesis is correct, those with family dynamics that align with the equation should report a greater sense of aliveness, suggesting that family is a crucial mediator in the relationship between our wild selves and the natural world.

For example, Immersing ourselves in nature and engaging in outdoor activities isn't just leisure; it's a gateway to enhancing our cognitive abilities and overall sense of well-being. Berman, Jonides, and Kaplan's (2008) seminal work demonstrates that nature interaction boosts cognitive functions, particularly memory and attention. Their research compellingly argues that a walk amidst greenery can do more for our brain's attentional capacity than a stroll through city streets, leading to improved cognitive performance and a refreshed mental state (Berman, Jonides, & Kaplan, 2008).

Moreover, natural settings have been shown to be a cornerstone for psychological restoration. Hartig, Evans, Jamner, Davis, & Gärling (2003) investigated the restorative effects of nature on psychological well-being, concluding that time spent in natural environments can significantly reduce stress. Their findings imply that our vitality is intimately connected to the natural world—a concept that might seem intuitive, yet is increasingly supported by empirical evidence (Hartig, Evans, Jamner, Davis, & Gärling, 2003).

The influence of nature on our mental state extends to children as well. Faber Taylor and Kuo (2009) explored how children with Attention Deficit Hyperactivity Disorder (ADHD) showed improved concentration after a walk in the park. This research indicates that the benefits of nature are not age-bound; the calming and focusing effects of green spaces can help children manage attention deficits and contribute to their sense of aliveness and engagement (Faber Taylor & Kuo, 2009).

Children's engagement with risk during play has also been linked to their development. Sandseter (2009) emphasized the importance of risky play in the development of children's physical and psychological resilience. By encountering and overcoming risks, children learn to navigate their environments confidently, which contributes to a robust sense of vitality and independence (Sandseter, 2009).

Lastly, the cultural narrative of risk aversion in child-rearing is challenged by the work of scholars like Gill (2007), who argues for the developmental benefits of embracing risk. His discussions suggest that exposure to risk is not something to be avoided but rather an essential element for cultivating competence and a zest for life among children, which can have long-lasting effects on their sense of well-being (Gill, 2007).

In summary, the more we connect with the outdoors and allow ourselves and our children the freedom to engage with risks, the more we stand to gain in terms of cognitive vitality and psychological health. This body of research invites us to reconsider our lifestyles and find more ways to integrate nature into our daily lives, for the sake of our own well-being and that of future generations.

Integration with Work (`W`)

Work is often seen as the antithesis of wildness, representing structure and order. Thank god for human creativity, it may be what transcends most of the suffering of not having a strong desire for meaningless dependencies on structure and discipline for success. Pressure can feel innovative and affectively lonely. However, the hypothesis posits that work can integrate with our wild nature and enhance our connection to life. In fact congruence is a therapeutic intervention. It’s also called mirroring in coaching as a power strategy. Testing this hypothesis would involve examining the relationship between true job satisfaction, the nature of the work performed, and the individual's sense of being alive. What makes feel more alive: nothing is more important that meaning. So work has a way of being rigid or weak with pressure. If successful is the goal then success = competency x flexibility. To win and feel agile, flexibility would need to be made equitable to competence, and its functional value, seen its relational factors to its true operational forces in meaning, something only necessary to maximize, versus reduce, and in order to grow competency work that is aligned with personal values and passions, perhaps even involving outdoor or nature-related activities, increase one's sense of vitality and aliveness.

Studies have measured the well-being of individuals in various professions, particularly comparing those with outdoor, nature-connected jobs to those in conventional office settings. Additionally, the impact of workplace culture on employees’ ability to express their wild nature could be assessed. If the hypothesis is correct, then work environments that accommodate the wild nature of employees and encourage their connection with nature should result in a higher reported sense of being alive, suggesting that the integration of work with our wild and natural selves is possible and beneficial.

Integration with Life (`L`)

Life beyond work can be seen as a canvas where our wild nature and connection to nature can be expressed more freely. To test the hypothesis "Alive = wild X Nature" in the context of life experiences, we can analyze how leisure activities, hobbies, and personal pursuits contribute to our sense of being alive. People who engage in activities that resonate with their inner wildness and allow them to connect with nature should, according to the hypothesis, report a higher sense of aliveness. This actually tested through surveys and studies that link participation in outdoor recreation, travel, and other natural experiences with metrics of psychological well-being. Additionally, examining the role of urban green spaces and their accessibility as facilitators for engaging with nature in daily life would provide insight into how urban living can align with the hypothesis. If the hypothesis proves accurate, it would suggest that life experiences that embrace our wild side and immerse us in nature are integral to feeling truly alive, regardless of the structure imposed by work and society.

Integration with the Balance Function Balance = (W + L)

Balance is flexible and winning. Something a lot like the neophyte of success, itself. Life is irrational and unpredictable, we only trust it when it is mad, insanely powerful enough to resist death, completely long eneough to reproduce too. So to carry on and win, this needs flexibility. Thus balance, with between fun and necessary work. Work isn’t pointless, its utility is to reprice both winning and comity. Continuity atomically means, flexibility, and wining can continue because it’s flexible. Competence is what evolution is here on earth. Competent in winning against death, thus to continue on, we need to be flexible, quite literally too. Two forms that reproduce or even one form that reproduces itself, is really about optimizing, and the shape of reproduction is seen in the symmetry and balance of the atomic meaning of continuity, flexibility. The irrational, it’s got hard limits, oppositional forces, and relational factors or flexibility. Mapping all oppositional forces The balance function in the hypothesis "Alive = wild X Nature" underscores the importance of harmony between work (W) and life (L) to maximize the sensation of being alive. The hypothesis is proven by studying individuals who have achieved varying degrees of work-life balance and their reported sense of vitality and connection to nature. A higher balance correlate with a stronger sense of being alive, as it allows for sufficient engagement with both structured work and the freedom to explore wild and natural pursuits. Research might involve psychological assessments and quality of life metrics, as well as examining the impact of workplace policies that promote work-life balance. Further, exploring how individuals navigate the tension between the demands of work and their desire for wild and natural experiences could provide insight into how this balance is achieved and maintained. If the hypothesis stands, it will be evident that work-life balance is a key factor in nurturing our wildness and fostering a deeper connection with nature, both of which are crucial for feeling alive. We are wild beings, maximized to be this way because of the need for flexibility. We are wild because this non negotiable need, basically.

Quantitative and Qualitative Analysis Applied to "Alive"

Quantitative and qualitative analyses provide different lenses through which to test the hypothesis "Alive = wild X Nature." Quantitatively, one only needs to collect data on health indicators, time spent in nature, engagement in activities that express wild. There’s a process. It’s all mapping the full meaning of success. You can’t lie by symbolizing truth, authentic and full value.

I’ll explain again, by exploring the interplay between work environments and employee well-being, several studies suggest that jobs with a connection to nature can significantly enhance one's sense of aliveness and contribute to recovery from stress. Barton and Pretty (2010) found that physical activities in natural settings, termed 'green exercise,' have positive effects on mental health, improving mood and self-esteem^(1). Office environments that incorporate plant life not only boost productivity but also increase workers' satisfaction and perception of air quality, as demonstrated by Nieuwenhuis et al. (2014)

The restorative effects of being in natural surroundings extend to stress reduction. According to Stigsdotter and Grahn (2003), individuals with access to a garden or green space report lower stress levels. Moreover, Berman, Jonides, and Kaplan (2008) provided evidence that interaction with nature could enhance cognitive functions such as memory and attention, beneficial traits for workplace productivity.

But how can this be, truly? How and why?

Success is not a monolith but a contextual phenomenon, shaped by balance and symmetry. The essence of reality—its objective truth—is mirrored in the outcomes that are well-rounded and harmonious. By mapping the relational dynamics to the forces that counterbalance our intentions, we craft truth. The physical world, when made to reveal truths, underscores the natural act of speaking truth to power.

Metaphors ground these concepts in tangible symbols, and success often finds its representation in plants. Unlike the fluffy bunny, a symbol less associated with the stark realities of life, plants and apex predators hold a mirror to the existential dance of balance. Plants, in their serene independence, need nothing but the bare essentials to thrive, existing with or without human intervention. Success, akin to plant life, is the product of competency and flexibility; it thrives on an unstructured mechanism that's not anchored to any single entity but rather to a supportive, growth-oriented environment.

Life itself is an audacious defiance of death—non-failing and fiercely independent. It unfolds regardless of individual preference, needing just warmth (a metaphor for affective flexibility) and sunlight (symbolic of power and vitality). The most competent creatures in the animal kingdom are often predators, which maintain ecological equilibrium by culling those that have grown complacent and excessive.

If apex predators did not exist to control the population of animals that prey on the weakest—including plants—our ecosystems would collapse. It is the independent producers, like plants, that create and sustain environments capable of self-reproduction. In contrast, a fluffy bunny, though seemingly innocuous, can be destructive in excess, as they could decimate plant populations if their numbers went unchecked.

Therefore, in the metaphoric language of existence, success relates more to the lion and the plant, symbols of purity and untamed reality. To succeed is to not fail—it's to embody the resilience and independence of these archetypes. The bunny, while endearing, is a metaphor for vulnerability and naiveté, a creature that, without the balancing act of the predator, would lead to its own downfall through unchecked consumption.

In the workspace, it is the presence of symbols like lions and plants that inspire and speak to a deeper truth of success—symbols that resonate with strength, adaptability, and the natural order of things. In contrast, the bunny vibe, though fictionally appealing, is complacent and does not invoke the competent drive for success. It is in this metaphoric dance of nature that we find the true embodiment of success, mirrored in the independence and balance that both the plant and the lion represent.

Workplace culture, especially when it facilitates a connection with nature, nature is meaning, because life can be sustained with just that, the will to live, and reproduce, and this has a profound impact on employees. Loyalty to highly organization, is debating to creativity. Life is visibly variance. The diversity we see isn’t a false relational factor. It’s representation of truth, quite purely, variance is flexibility and that’s recharging. Life says being an emodiement and function of that. Creativity transcends, you can’t understand abstract concepts like success, without understanding the embodiment of flexibility.

Work feels dehumanizing because it’s rigid and appropriate. Rigidness to loyalty to have any structure but one that reproduce, we all come from variance of flexibility and winner. So symbols that mean more truth, are most important, they influence our subjective perception, the things we are irrationally attatched to. Like life = warmth and power. All life need precise physical temperature, and there is some Flexibility but death is really flexible. Life is also irrationally attatched to defying death, very arrogantly and such arrogance is relentless but certain tempered, freeze or cook. So warmth is irrationally attatched to womb, humans were not cooked there so its balance, irrationally a mid tempature. Not too much or too less, and when its a lot like an apex predator, its only legit when its harm ultimately is not harming but apart of the shape, a style of balance, the symmetry of either complimenting reproduction of life, or symmetrical, but never against; that’s death.

Mishra and Gupta (2014) highlighted how a workplace culture that resonates with natural elements can promote employee engagement and satisfaction. The Terrapin Bright Green report (2012) supports this, indicating that biophilic design in the workplace leads to better performance, reduced stress, and fewer sick days. Finally, the natural 'wildness' and creativity promoted in work environments can bolster problem-solving skills, as Kaplan and Kaplan (1989) have shown.

These findings align with the hypothesis that workplaces which foster a connection with nature can enhance the well-being of their employees, supporting the integration of our innate wildness into our professional lives.

References:

Barton, J., & Pretty, J. (2010). What is the best dose of nature and green exercise for improving mental health? A multi-study analysis. Environmental Science & Technology, 44(10), 3947-3955. https://doi.org/10.1021/es903183r

Nieuwenhuis, M., Knight, C., Postmes, T., & Haslam, S. A. (2014). The relative benefits of green versus lean office space: Three field experiments. Journal of Experimental Psychology: Applied, 20*(3), 199-214. https://doi.org/10.1037/xap0000024

Stigsdotter, U. K., & Grahn, P. (2003). Experiencing a garden: A healing garden for people suffering from burnout diseases. Journal of Therapeutic Horticulture, 14, 38-48.

Berman, M. G., Jonides, J., & Kaplan, S. (2008). The cognitive benefits of interacting with nature. Psychological Science, 19(12), 1207-1212. https://doi.org/10.1111/j.1467-9280.2008.02225.x

Mishra, L., & Gupta, S. K. (2014). Workplace culture and the psychological contract. In The worker passion framework: An emerging perspective on worker engagement (pp. 1-10). Emerald Group Publishing Limited. https://doi.org/10.1108/S2046-410X20140000004016

Terrapin Bright Green. (2012). The economics of biophilia: Why designing with nature in mind makes financial sense. Terrapin Bright Green LLC.

Kaplan, R., & Kaplan, S. (1989). The experience of nature: A psychological perspective. Cambridge University Press.

Berman, M. G., Jonides, J., & Kaplan, S. (2008). The cognitive benefits of interacting with nature. Psychological Science, 19(12), 1207–1212. https://doi.org/10.1111/j.1467-9280.2008.02225.x

Hartig, T., Evans, G. W., Jamner, L. D., Davis, D. S., & Gärling, T. (2003). Tracking restoration in natural and urban field settings. Journal of Environmental Psychology, 23(2), 109–123. https://doi.org/10.1016/S0272-4944(02)00109-3

Faber Taylor, A., & Kuo, F. E. (2009). Children with attention deficits concentrate better after walk in the park. Journal of Attention Disorders, 12(5), 402–409. https://doi.org/10.1177/1087054708323000

Sandseter, E. B. H. (2009). Affordances for risky play in preschool: The importance of features in the play environment. Early Childhood Education Journal, 36(5), 439–446. https://doi.org/10.1007/s10643-009-0307-2

Gill, T. (2007). No fear: Growing up in a risk averse society. Calouste Gulbenkian Foundation.

[djwegmans]

Behavioral Analysis

The topic I wish to discuss, the quantitative analysis of human behavior, is one that I have examined for several years. I hope to continue to expand my observations on this topic for many years to come, as I find it captivating and believe that it has many different applications in daily life. I wish to preface this discussion by clarifying that I do not have professional authority in this area, and do not believe that my interest in this topic constitutes science. Despite this, I believe that methodically examining one’s behavior in the way that I describe can approximate some of the utility of science, insofar as I believe that it will provide readers valuable insights into trends and patterns that exist in their lives, and perhaps ways to overcome challenges constructively. The realm of science is the demonstration that empiric observations do not falsify theories about the structure of reality as it presently exists in a general sense and did in the past as well. Analysis of one’s own behavior is intrinsically a much more subjective exercise and not generalizable to others or to the description of a generalized model of behavior. However, I hope by writing about this topic to convince the reader of its usefulness to their own life. I also hope to contend that quantifying behavior in a more formal context may one day have valuable applications within the realms of science and medicine. Perhaps if daily behavior is measured in a relatively objective manner in the longitudinal and quantitative manner, I describe it can begin to be implemented into formal scientific studies as a useful variable.

My examination of behavior essentially began as an offshoot of my journal, which I keep in the form of an excel spreadsheet titled by the present year (i.e., “2021”). I write entries throughout the year and begin a new spreadsheet each January first. My journal entries are listed by date in one column and entry in the next. I also tag journal entries with different letter abbreviations to describe content, i.e., “w” for writing ideas, “d” for dreams, “t” for thoughts, or “s” for scientific ideas. If one day’s entry discusses multiple topics, I separate these topics with forward slashes (“/“). Although excel began as a convenient way for me to catalogue my thoughts, I have gradually expanded my use of it over many years to record various types of information about my life. Besides a journal I have sections of my spreadsheets that I use to keep track of my calendar (personal and professional), movies I have watched (and with whom I have watched them), books I have read, addresses, and birthdays. At one point I separated these sections into different tabs but have more recently consolidated them onto a single tab entitled “Journal.” 

I use a second tab, “Finance,” to keep track of my expenses. I began monitoring my finances in a manual fashion around 2015 to keep track of my spending and with the specific goal in mind of spending less yearly on food. I am not an exceptional cook and eat out too frequently. Keeping a log of my personal finances also afforded me the opportunity to learn more about various topics in finance that interested me, such as simple moving averages. Finally, I used this tab as an excuse to learn more about various excel formulas, which I used to partially automate my record keeping process. One column of my finance tab records expense descriptions, i.e., “Walmart” or “gas,” while adjacent columns list price and date of purchase. Another column to the right records the amount spent per week by category of expense, such as groceries or transportation, with embedded excel formulas used to automatically record these sums based on the week’s various expenses and their descriptions (i.e., this formula ensures that all purchases from “Giant” are added to the “grocery” total). These subtotals are then summed to give the week’s total expenses. Columns across the top of the Finance tab consolidate this information across the entire year, with one row for each week, and various columns for total weekly expenses, total weekly grocery expenses, total weekly transportation expenses, etc. Additional columns keep a running tally of averages and 10-week simple moving averages, and a final row is used to project the likely yearly expenses based on current total expenses and the proportion of the year that has elapsed. 

I use a third and final tab of my spreadsheet, “Health,” to keep track of behavior. This third tab initially began as a way for me to keep track of my running, which I enjoy but am decidedly mediocre at. To motivate myself to run more, I would log every run with the time ran in one column and the date in an adjacent column. Eventually I began tracking weightlifting as well, and had one column for date, one column for running, and a third for weightlifting. Subsequently, I expanded the use of this tab beyond exercise and began recording the amount of time that I mediated throughout the week, the amount that I read, and the amount that I cooked to encourage myself to do these activities more often as well. Initially, I would keep track of time using the same watch I took with me when I went running - a very simple black $5 watch that I purchased at Walmart a few years ago. I kept a running total of the amount of each activity I did throughout the year. It was very gratifying to watch the totals slowly rise over the course of the year, and over several years I could compare my habits and determine whether I was becoming healthier (exercising, cooking, and meditating more) and more scholarly (reading more). In this way I have found that monitoring my behavior quantitatively to be intrinsically motivating and fulfilling. I have tried to avoid being too anal retentive and needlessly recording information about all the recurring activities of my daily life. I have restricted myself to those that I wish to do more because I find them particularly gratifying, interesting, or good for my health (running, reading) or those behaviors where performing them more frequently would confer some essential utility to my daily life (cooking, grocery shopping).

I discovered a more concrete, practical application for this tab when I began medical school. As you may know medical school demands students read a very high volume of material (thousands of hours) to pass their classes, pass their board exams, and complete their degrees. These classes encompass a wide variety of topics, from the basic sciences pertinent to medicine (i.e., biochemistry, genetics) to the body’s various organ systems (cardiovascular, gastrointestinal), to the practice of various medical specialties (internal medicine, surgery). Each semester of classes builds on the previous and it is essential for all students to keep up with their readings. There are simply no shortcuts to the accumulation and consolidation of this body of knowledge, which continues to grow larger every year. Keeping track of the amount of time that I spent studying on a daily, monthly, and yearly basis was critical for me to pass my exams. Some students are gifted with incredible memories and only need to go over material once to memorize it and recall it on exams. I am not so fortunate and required what I believe to be somewhat longer than average to receive passing grades. I could not have effectively kept track of the amount of time I spent reading without this spreadsheet tab. It allowed me to establish weekly standards for myself (i.e., study four hours a day) and also helped me to anticipate how much I would need to study for certain classes (i.e., if a certain class required 50 hours of studying for a passing grade, another class known to be similarly challenging would likely require a similar volume of studying). Initially I used my watch to keep track of the amount of time I read. Later, I switched to mainly studying off my iPad and using the “screen time” function available in settings. This allowed me to identify the amount of time I spent specifically on apps dedicated to my studies (i.e., UWorld, Acrobat) without accidentally rewarding myself for distracting myself with social media or the internet.

Recording the time I spent studying methodically in a spreadsheet also allowed me to graph my studying habits throughout the year and observe visual trends. Besides graphing weekly totals of the amount of time I spent studying throughout the year, I eventually also decided to chart running averages and 10-week simple moving averages as I did with my finances. After about two years of tracking these averages, I wholesale converted the format of the “Health” tab to reflect the “Finance” tab. I overall found this format useful for recording quantitative information about different types of behavior and monitoring long term trends. Recording behavior in this manner also allowed me to fluidly vary the specific behaviors I tracked as my interests or goals changed while still tallying totals across a few global categories (i.e., two different study techniques I used often in medical school, Anki and UWorld, as well as the amount of time I spend on research projects, are all tallied in the study column, whereas running and biking are both tallied in the exercise column). In this way I began to scrutinize my behavior in much the same way that I analyze my finances or the prices of stocks and began thinking about their trends in a similar fashion (i.e., what caused them to go up or down at certain points in time, and what they were likely to do in the future if certain conditions presented themselves).

After charting my behavior for a few years, several patterns began to emerge. First, I noticed that even if activity trended in a certain direction over the course of the year (i.e., I studied more as classes became more difficult) that weekly totals fluctuated constantly. Some weeks I would study more and some I would study less, and as much as I sought to simply “study more” the reality was that I was not able to improve my habits with robotic efficiency. Secondly, different habits exhibited different trends throughout the year. During some parts of the year, I would exercise more, during other parts I would read for pleasure more, and still other parts I would study more. These periods would sometimes correspond with one another, but sometimes not. With regards to this point, my behavior has exhibited several trends throughout the years - periods of high activity of specific behaviors over the course of many weeks, often in response to certain environmental stressors. One such example: I spent much more time meditating while studying for my board exams (likely to avoid placing too much stress on myself and to take periodic breaks from reading). Another such example: I tend to exercise the most in the spring and late summer when it is most comfortable to run outside. In these examples I cannot demonstrate any level of causation between environment and behavior but only say anecdotally that I seem to observe these trends and am able to identify environmental factors present at those times. Whether there is a link between the two I can only speculate but I can say with some confidence that I believe there is a relationship between the two. If I were to try to establish a more concrete link between behavior and environment, I believe that I would probably try to record variables over time outside of behavior and look for correlations between the two (i.e., record weight at the same time every week and look for a correlation between exercise and weight). Of course, as noted above, if I were to ever desire to demonstrate that relationships existed between behavior as tracked in this manner and environment with any level of scientific accuracy, it would be necessary to conduct a study with many participants and most likely a more objective means of behavioral record keeping than self-reporting. I will conclude this point by noting that although I sought to identify (or perhaps impose) some sort of order on my behavior by charting it, the more precisely I observed it the more organic it appeared, invoking a reflection of the body’s natural circadian rhythm on a longer time scale. However, I have observed a few trends over the course of several years that I believe are not wholly organic but are in fact a direct result of my behavioral analysis to begin with. The first year that I recorded behavior formally, 2019, I logged 108 minutes of activity a day across multiple categories. This number has risen successively over the course of the last four years, and I currently am recording about 7.6 hours of activity a day. Even though I have gotten slightly busier over the course of the past four years, I believe that tracking my behavior and specifically the amount of time dedicated to useful activities, the better my time management has become. This has afforded me more time to spend on the parts of life that are most important to me. I believe that my attention span has also improved, which I would here offer the amount of time I spend weekly reading for pleasure (3 hours in 2019 and 5 in 2022) as an informal proxy. Consequently, at this point I would contend that quantitatively analyzing behavior over a long period of time is not merely a useful way to identify trends but also to condition behavior.

I mentioned meditation above and would like to place more emphasis on the utility of rewarding oneself for recording information about this category of behavior. By meditation I mean a few different activities that I group in the same category of the Health tab, titled “Mindfulness.” First, sitting quietly for a dedicated period and thinking about things that have happened recently in my life without distraction. I permit myself to drink some tea if I am so inclined. Next, going for walks throughout the day, which I always find are able to help me clear my head. Finally, and I cannot stress enough the utility of this choice enough, dedicated blocks of time during the day when I do not check the internet, social media, or my email. To keep up with essential electronic communications, during these blocks of time, I permit myself to check my messages and work email at 8 am, 12 pm, 4 pm, and 8 pm. Otherwise, I avoid electronics completely (except for, for example, my iPad on airplane mode for studying purposes) to remain focused on other activities. Then, when I record this block of time in the Health tab, I subtract the total amount of time I have spent on my computer and phone during the day (again using the “screen time” function) to discourage nonessential use of electronics. I have found that the more effectively I have learned to carve out specific blocks of time away from electronics to focus on various tasks, the more my attention span and memory have improved. I also reward myself if I avoid electronics completely between evening and morning by recording this interval and have found that developing discipline in this realm has greatly improved my sleep hygiene. I would subjectively say that my sleep quality has improved over the past few years, and I also find that I am better rested and more focused during the day. 

These methods may possibly seem excessive, but it is the alternative I have willingly chosen to several (in my opinion) unhealthy coping strategies that society tolerates as ways to get through the day such as smoking, alcohol, and the various distractions available on television and the internet. With regards to the latter, I believe that while the internet is a very powerful, useful, and essential tool in the Information Age, we are fallible as human beings to using it for vice. It is an easy way for us to distract ourselves from our daily, necessary work. However, the more dependent we grow on it, the more damage I believe it has the potential to inflict on our attention spans and mental fortitude. The internet has now become a deeply integrated component of our daily lives: people spend an average of 3 hours and 15 minutes on smartphones and check their phones an average of 58 times a day. Interestingly, this figure varies widely by both country and age, suggesting that there are several cultural factors at work influencing daily smartphone use and likely technology use altogether. This is more time than I generally spend exercising, reading, and cooking altogether on any given day. Currently, per screen time data, I spend about 2.5 hours a day on my iPhone but hope to decrease this figure moving forward to perhaps 1.5 hours if possible. I believe that it is essential that we regulate the use of this tool which has become such an essential (but at the same time, I fear, addictive) element of our daily lives, so that it does not supplant more critical components of our hierarchy of needs. To summarize, mindfulness has received increasing attention over the past several years as an important element of health and I could not agree with this trend more emphatically. Mental health and physical health are intimately interconnected, and it would be a critical error for me to recommend the analysis of behavior relating to one’s physical health while neglecting mental health as well. 

Relating to our hierarchy of needs, I have also used my spreadsheets to reward socializing. I started using my Health tab in this manner when I noticed that I was becoming lonelier and more isolated during the middle of medical school after a difficult break up. My studies were quite challenging during this period, and it was very difficult to justify spending my time on almost anything else. Carving out dedicated blocks of time to call my friends on the phone or go to dinner with them helped me to rise above my partially self-imposed isolation and get past the behavioral inertia that was leading me to study at the expense of other, equally important parts of my life. While I keep track of the amount of time that I dedicate to my friends and family to ensure that I do not needlessly isolate myself in my studies, I believe that other individuals could find many other potential applications to this kind of timekeeping. Perhaps individuals with social anxiety disorder could reward themselves for going to parties or on dates, for example. I would further contend that individuals with ASD may find this sort of timekeeping a useful way to understand on a deeper level the amount of time required for activities of daily living (i.e., cooking, grocery shopping) and to manage their time more effectively as adults in particular.

I would say that what I have accomplished with the guidance of my Health spreadsheet is quotidian. I am not an exceptional athlete or student. The furthest I have ever run is a half marathon, and most of my classmates are smarter and more successful than I am. However, I will say that my spreadsheets helped me to accomplish some of my goals, such as passing my medical board exams, and that I might not have accomplished these goals otherwise. I hope for the reader, by applying my thoughts and recommendations to their own lives, to achieve even greater ends themselves. I believe that tracking behavior has value for students and athletes, people struggling with social anxiety, and even individuals who are simply busy and wish to manage their time more effectively. At a few different points, I have sought to convert my spreadsheets into an app with the hopes of making my ideas more accessible to others. However, I continue to regard excel as a very powerful piece of software – easy to use, adaptable to numerous different applications, and conveniently accessible from any computer or phone using google drive. Additionally, the software’s features make it easy to graph longitudinal behavioral data and analyze trends. As a result, I continue to use excel, believing no alternative to be superior.

Finally, if this topic still seems banal to the reader, I will contend that there is perhaps no more valuable resource than time in the Information Age, and no more critical task than its appropriate use or allocation. The global population may continuously rise, and inflation may continuously diminish the value of the dollar, but time remains absolute and unchanging, like the total mass of gold on earth. There will always be seven days in a week, and we will only ever have a finite amount of time in our lives to expend towards our various endeavors. Every day, many different venues of activity and entertainment compete for our time. However, we ultimately have the agency to decide how to spend our time within reason and the constraints of our individual circumstances. I will end this thought with a hypothetical question which inverts my argument: if our free time is not valuable, why do so many forms of entertainment and advertising compete for it on a daily basis? Furthermore, how much money are those companies generating from the amount of time that you spend engaged with their products, digital or physical? My personal response to this hypothetical question is as follows: I believe that the old platitude, “time is money,” is likely true.

[clinthora2024]

The Future of EHR EMR Software: Trends to Watch in 2025

We are on the edge of a technological advancement in the healthcare sector, and clinic management software and EHR-EMR software are getting the much-deserved importance they deserve in 21st-century medical practices. These systems have become a must-have in all practices that wish to provide quick and effective care to patients. Along comes 2025, and the landscape of EHR EMR software will transform the way clinics are run in ways that allow them to overcome even the most complicated challenges in their practice.

This case discusses the history of EHR EMR software, focuses on its clinic management software side, gives an overview of its advantages, and reviews the prognosis of EHR EMR software in the future. Let us explain how the tools are transforming the health care systems, making the processes more efficient, and preparing for the management of the clinic in a better and smarter way.

Concept of EHR EMR Software

To evaluate the improvement in EHR EMR software, it is essential to dig deeper into the very essence of it. Electronic Health Record (EHR): An EHR is an accessible longitudinal record of a patient’s health history and all of the information that can be found across multiple healthcare providers.

Electronic Medical Record (EMR): Only admitting and treating healthcare facilities use this type of medical record. It is in the form of a paper chart, which is scanned to create a digital image.

Both systems are adopted in clinic management software to ensure compatibility in booking appointments, keeping patient records, and billing. It is difficult to imagine modern clinics without them aiding data management.

EHR EMR Systems: Why Are They Important?

Enhanced Patient Care: That is, keeping complete and up-to-date records of patients.

Streamlined Operations: This includes incorporating billing, appointment bookings, and prescription writing.

Compliance Management: This assists clinics in the compliance of governing legislation such as HIPAA or regulations of the Indian Medical Council.

Heightened Communication: Coordination of activities among providers, patients, and other systems such as laboratories and pharmacies.

Advantages of EHR EMR Software

Productivity Enhancement

Further, the more modern practices are now depending on the clinic management systems in order to do some of the routine operations like reminders, billing, and follow-ups. The workflow of EHR EMR integration removes unnecessary data entry and transitions between clinic and business processes very smoothly.

Data Protection

As society evolves, discussions on data security issues have grown to be one of the major considerations in any industry. This is due to the advanced security features that are available in modern EHR EMR systems, where information on patients is safe from any hacking activities even with the use of high encryption and multi-factor systems. Adherence to these goals is also aimed at the current trends, which, for example, are the GDPR in more than one zone.

Access to data at any time

Regardless of where the clinician is, the clinic, or the telehealth session, they are still able to view patients’ records at that moment. This level of accessibility enhances the precision of diagnosis and ensures that there is a differential treatment plan for each patient.

Increased Interaction with Patients

One of the advanced clinic management software includes a patient portal integration that allows patients to make appointments, check lab results, and manage prescriptions themselves. Making such information available generates trust and improves participation.

Evidence-Based Management

The modern EHR EMR programs also have an analysis component that allows the medical centers to make business decisions. Such insights help in moving the needle on anything that can be monitored, from patient outcomes to the bottom line.

Problems Related to EHR EMR Implementation

Although there are plenty of benefits of EHR systems, adding EHR or EMR software into clinic operations comes with problems as well.

Preliminary Costs and Setup

Most clinics do not go for EHR EMR solutions because of the high initial budget requirements. On the other hand, the use of management software that is scalable to fit a clinic's needs may help ease the financial burden and provide reasonable annual subscription services.

Issues on Data Migration

The process of moving information from old EHR EMR systems to new ones can be a challenge. There is, however, the need to ensure that systems are compatible and staff are trained well on the new systems.

Resistance to Change

Those personnel who have little or no skills in utilizing computerized platforms will naturally not welcome the idea. Such resistance can be countered by training and designing ergonomic interfaces on the clinic management software.

Shortage of Interoperability

Another challenge is that progress has not been made towards enabling interaction between different EHR EMR systems. As the evolution of standards continues, focus on the aspect of interoperability will be inevitable.

Worries About Protection of Information

Despite the war on data breaches being taken three steps higher due to encryption and measures put in place to ensure compliance, data breaches are still a real threat. There is a need for clinics to incorporate systems that are adequate and geared towards cybersecurity.

Future Trends in EHR EMR Software for 2025

EHR EMR software will always tend to be focused on innovation and evolution. The following are the most significant trends that we expect to see in the year 2025:

Assimilation of Artificial Intelligence (AI)

The improvement of EHR and EMR systems will significantly change the traditions of arrangements of clinics with patients' data. AI solutions like chatbots will also be useful in patient engagement and administrative management.

Collecting of Telehealth Information

The art of medicine has a highly promising future, and teleconsultation will be incorporated into existing EHR and EMR setups without hitches. With this feature, doctors are able to update the patient’s records even when carrying out a video consult, thus maintaining the constant care cycle.

Security and Data Privacy Management Using Blockchain Technology

Data security is bound to undergo revolutionary changes with the advent of blockchain technology. It resolves trust issues by providing unalterable records of transactions and removing the need for a central repository.

Improvement in the Levels of Interoperability

There are ongoing efforts to ensure that certain data exchange protocols are adopted by all EHRS/EMRS systems. A resultant effect of this interoperability will be the increased cooperation between different healthcare providers.

Deployment of EHR EMR Software Solutions in Mobile Devices

More and more clinics will be implementing mobile versions of EHR EMR systems, allowing doctors to not only carry patient information but also file it using a mobile phone or a tablet accordingly.

Clinic-Specific Tailoring

Future software for clinic management will include extended EHR EMR modules that are highly adjustable for each clinic to implement upon their departmental operations and areas of practice.

More Emphasis on Patient-Centric Adoption

The trend of involving patients more actively in the treatment management will be more pronounced than before. It’s expected that patient portals, health monitoring, and automated reminder functionalities will be the norm.

Insights that Clinics can Utilize

In order to remain competitive, clinics must take concrete measures that involve the incorporation of the latest available trends in the use and implementation of EHR EMR software. Here’s how to do this:

Conduct a needs assessment: examine if the current practice management system allows the integration of the latest EHR EMR technologies and functions.

Allocate funds for education: familiarize the personnel with advanced features to ensure effective application of the same.

Work with a Trusted Partner: Select systems such as Clinthora—those that are secure, scalable, and compliant with the requirements of the clinics.

Go for Telehealth: Incorporate such systems to allow for remote provision of health services, considering clients are nowadays digitally centric.

Focus on Prevention: Choose systems that have advanced encryption and certifications, are updated often, and have security features.

Concluding Remarks

Looking forward to the year 2025, the evolution of clinic management software and EHR EMR software is very encouraging. EHR and clinic management software help clinics achieve operational efficiency and enhance the quality of care given to patients while being proactive about the changes taking place in the industry. Nevertheless, the right software must be purchased, the technology development trends must be followed, and the issues must be resolved.

With this transformation, we at Clinthora are dedicated to providing clinics with the best solutions with functionality, security, and compliance. The digital age is now; get on board with Clinthora!

[warningsine]

While the market for menstrual tracking apps is rapidly growing, there have been no large-scale analyses about why users download these apps, and particularly few studies on their use in the Global South.

Using data from the Google Play Store and Apple App Store between April and December 2021, the study explores why users download these apps in the Global South and North.

Three apps - Clue, Flo and Period Tracker - dominated the market, with over 250 million downloads combined. Most downloads of menstrual tracking apps were concentrated in the Global North, but there were also downloads throughout the Global South, with a particularly high prevalence in South America. Of the 112 countries included, the study found that low-income countries with a higher unmet need for family planning and higher total fertility rate are associated with more downloads.

Lead author Dr Francesco Rampazzo, Lecturer in Demography at Oxford’s Leverhulme Centre for Demographic Science and Department of Sociology, said, ‘Our findings reinforce emerging evidence that menstrual tracking apps are more popular in areas with limited access to reproductive health services and contraception. This highlights the important role these apps may potentially play in improving access to reproductive health information and services, especially in low-income countries.’

The study employed various analytical methods and a Bayesian model to estimate downloads for 25 menstrual tracking apps that had at least 3,000 installations, 10 reviews, and 60 ratings. The authors also analysed reviews left by the users of these apps between 2009 and 2021. Using a language processing model, the researchers identified 19 topics for app usage, 12 of which were related to reviews. Menstrual cycle tracking was the most common reason reported for using these apps (61%), followed by achieving a pregnancy (22%), a sense of community (9%), and avoiding getting pregnant (8%).

Co-author Dr Alyce Raybould, Research Fellow and Survey Manager at the University College London’s (UCL) Centre for Longitudinal Studies said, ‘Our study suggests that while many use these apps to understand more about their reproductive health and menstrual cycles, others are using them to help avoid pregnancies. This warrants further investigation to see how these apps could be affecting outcomes like unplanned pregnancies, given that a very limited number of these apps market themselves as a contraceptive tool.’

The study shows that menstrual tracking apps are used worldwide, even in low-income countries, though usage is lower in areas like Sub-Saharan Africa and Central Asia, likely due to internet access and economic barriers. The authors note that most apps are designed with Western assumptions, which may limit their cultural relevance and effectiveness. This highlights the need for research into their impact on reproductive health, particularly in areas with limited health services.

Dr Francesco Rampazzo adds, ‘Our study highlights the potential of menstrual tracking apps to empower users in managing their reproductive health. By understanding the global trends and motivations behind app usage, we can better address the needs of diverse populations.’

These research questions also connect to broader policy areas, such as the United Nations Sustainable Development Goals (SDGs) on women’s reproductive health and well-being, as well as their access to technology, resources, and information.

As the study’s findings are limited by potential disparities in data availability and quality between regions, the researchers call for further research to track changes in app usage and its impact on reproductive health over time.

Given the global popularity of these apps, the authors urge policymakers to take timely action, as existing research raises concerns about the information disseminated by private corporations and the monetisation of individual-level data collected from users worldwide.

Co-author Dr Douglas Leasure, Senior Researcher and Data Scientist at the Leverhulme Centre for Demographic Science and Oxford Population Health’s Demographic Science Unit said, ‘While it is encouraging that these menstrual tracking apps could empower women in locations with unmet family planning needs, we also hope these results will spark a conversation about potential risks when private-sector app developers fill in for reproductive health professionals.’

[gtsconsultantin]

The Convergence of Machine Learning and Medicine: Essential Datasets to Consider

Introduction:

The convergence of machine learning (ML) and Machine Learning Medical Datasets is significantly reshaping the healthcare sector. From enhancing diagnostic accuracy to enabling personalized treatment plans, ML algorithms are revealing new opportunities to elevate patient care. Central to these advancements are extensive and carefully curated datasets, which serve as the backbone for training, testing, and validating ML models, thus becoming indispensable resources for both researchers and healthcare professionals.

For those venturing into this dynamic field, the following overview highlights some of the most notable medical datasets that are pivotal to the advancement of medicine.

Datasets for Image and Video Annotation

Medical imaging is fundamental to diagnostics, with modalities such as X-rays, MRIs, and CT scans being integral to numerous treatment protocols. ML models trained on labeled medical images can accurately detect anomalies, including tumors and fractures.

CheXpert: This extensive dataset of chest X-rays includes annotations for prevalent thoracic conditions and is widely utilized for developing algorithms in pulmonary medicine.

LUNA16: Concentrating on lung cancer detection, this dataset comprises annotated CT scans that facilitate the creation of models aimed at early cancer diagnosis.

MICCAI Challenge Datasets: These datasets are associated with annual competitions and encompass challenges related to brain tumor segmentation, liver lesion detection, and more.

For organizations that specialize in image and video annotation services, such as GTS, these datasets provide a foundational resource for annotating and tailoring data for specific applications.

Datasets in Genomics and Proteomics

Machine learning is transforming the field of genomics by enabling the analysis of intricate DNA sequences and the identification of disease-related mutations.

The Cancer Genome Atlas (TCGA): This extensive dataset encompasses genomic information for various cancer types, supporting research aimed at developing targeted therapies.

1000 Genomes Project: This initiative focuses on human genetic diversity, providing valuable insights into genetic variation.

Electronic Health Records (EHRs)

EHRs offer comprehensive longitudinal data that document patient histories, treatments, and outcomes. When integrated with machine learning technologies, they can forecast patient risks and enhance care pathways.

MIMIC-III: This publicly available database contains de-identified EHRs from over 40,000 critical care patients and is widely utilized in the development of clinical decision-making models.

eICU Collaborative Research Database: This dataset is dedicated to critical care research, allowing investigators to analyze mortality predictions, readmission risks, and other related factors.

Public Health and Epidemiology Datasets

Public health datasets are essential for gaining insights at the population level, aiding in the identification of disease trends and the assessment of intervention effectiveness.

CDC WONDER: This platform grants access to a diverse array of public health datasets, including statistics on mortality and vaccination rates.

COVID-19 Open Research Dataset (CORD-19): In response to the pandemic, CORD-19 provides an extensive collection of scientific literature pertaining to COVID-19.

NHANES (National Health and Nutrition Examination Survey): NHANES gathers data on health and nutrition, facilitating research on chronic diseases and the effects of lifestyle choices.

Natural Language Processing (NLP) in Medicine

Textual datasets play a vital role in the development of NLP models that can derive insights from clinical documentation, scholarly articles, and patient evaluations.

PubMed Central (PMC): This free repository of biomedical and life sciences journal articles facilitates advancements in NLP for the analysis of medical literature.

n2c2 NLP Challenges: These datasets are designed to focus on the extraction of structured data from unstructured clinical documentation.

How to Begin with Medical Datasets

Utilizing these datasets necessitates specialized knowledge, effective annotation services, and a comprehensive understanding of ethical issues, particularly concerning patient confidentiality and data protection. Organizations such as GTS offer customized annotation services to improve the quality and applicability of medical datasets, ensuring that your machine learning models are constructed on solid foundations.

Concluding Remarks

The incorporation of machine learning into the medical field represents a transformative shift, with datasets acting as the fundamental basis for innovation. Globose Technology Solutions Whether you are creating diagnostic tools, predictive models, or algorithms for treatment optimization, comprehending and leveraging these datasets can significantly advance your initiatives. As the domain evolves, the potential to utilize machine learning for enhanced healthcare solutions will continue to expand.

[philomathresearch]

Key Elements of Quantitative Market Research in the Healthcare Industry

In the ever-evolving landscape of healthcare, decisions must be backed by accurate data and actionable insights. Quantitative market research in healthcare emerges as an indispensable tool, empowering stakeholders with numerical data to make informed choices. From understanding patient preferences to identifying market trends, quantitative research plays a pivotal role in improving healthcare outcomes and driving business success.

This blog delves deep into the essentials of quantitative market research in healthcare, exploring its importance, methodologies, applications, challenges, and future trends.

Understanding Quantitative Market Research in Healthcare

Quantitative market research involves collecting numerical data to identify patterns, test hypotheses, and make data-driven decisions. In healthcare, this research focuses on understanding consumer behaviors, measuring service effectiveness, and predicting future trends.

Key characteristics include:

Structured data collection: Surveys, polls, and questionnaires.

Statistical analysis: Tools to interpret data patterns.

Objective insights: Numerical evidence supports conclusions.

Importance of Quantitative Market Research in Healthcare

Healthcare is an industry where precision matters. Quantitative research offers:

Data-Driven Decision Making: Hospitals, pharmaceutical companies, and policymakers rely on statistics to make critical choices.

Patient-Centric Care: Quantitative insights help understand patient needs, improving care delivery and satisfaction.

Market Trends Analysis: Identifies emerging technologies, treatments, and competitive positioning.

Regulatory Compliance: Ensures adherence to stringent healthcare standards and policies.

Core Methodologies in Quantitative Healthcare Research

Quantitative market research leverages various methodologies tailored to healthcare needs. Below are the most prominent methods:

1. Surveys and Questionnaires

Description: Structured tools with multiple-choice questions or Likert scales.

Applications: Patient satisfaction surveys, feedback on new treatments.

Advantages: Cost-effective, scalable, and time-efficient.

2. Longitudinal Studies

Description: Studies conducted over an extended period to observe trends.

Applications: Tracking chronic disease management or medication adherence.

Advantages: Provides in-depth insights into changes over time.

3. Experimental Research

Description: Controlled environments to test the impact of variables.

Applications: Clinical trials for drug efficacy or behavioral experiments.

Advantages: High accuracy and reliability.

4. Data Analytics and Secondary Research

Description: Analyzing pre-existing datasets like electronic health records (EHRs) or government reports.

Applications: Population health management, cost optimization studies.

Advantages: Saves time and leverages vast datasets.

Applications of Quantitative Research in Healthcare

Quantitative market research serves diverse purposes across the healthcare ecosystem:

1. Pharmaceutical Research

Market sizing for new drugs.

Tracking the success of launched products.

Evaluating consumer perceptions of drug safety and efficacy.

2. Patient Experience and Satisfaction

Measuring satisfaction with healthcare facilities.

Identifying gaps in patient care delivery.

3. Health Technology and Devices

Evaluating the usability of wearable devices and apps.

Predicting adoption rates for telemedicine platforms.

4. Healthcare Policy and Strategy

Assessing the impact of public health campaigns.

Supporting policy decisions with population-wide statistical data.

5. Brand Positioning and Competitor Analysis

Benchmarking against competitors in terms of services, technology, and innovation.

Tracking brand awareness and loyalty metrics.

Challenges in Conducting Quantitative Healthcare Research

While quantitative research is essential, it is not without its challenges. Key obstacles include:

1. Data Privacy Concerns

Adhering to regulations like HIPAA and GDPR to protect patient information.

2. Sampling Bias

Ensuring representative samples for accurate generalizations.

3. Data Accuracy

Minimizing errors in data collection, especially when participants self-report.

4. High Costs

Implementing large-scale surveys or clinical trials can be resource-intensive.

5. Integration of Data Sources

Combining data from disparate systems like EHRs, patient surveys, and administrative records.

Best Practices for Conducting Quantitative Market Research in Healthcare

To overcome challenges and achieve reliable results, researchers must adhere to these best practices:

Define Clear Objectives Start with precise research goals to guide the methodology and analysis.

Ensure Representative Sampling Use diverse and inclusive sampling techniques to avoid bias.

Leverage Technology Use advanced tools for data collection and analysis, such as AI and machine learning.

Focus on Data Security Encrypt sensitive data and follow stringent privacy protocols.

Collaborate with Experts Work with clinicians, statisticians, and technology professionals for comprehensive insights.

Iterative Testing Validate findings through pilot studies or repeat surveys to ensure reliability.

The Future of Quantitative Research in Healthcare

Healthcare is rapidly evolving, and so are the methods of market research. The future of quantitative healthcare research will likely be shaped by:

1. Integration of Big Data

Massive datasets from wearables, EHRs, and IoT devices will enrich research quality.

2. AI-Driven Insights

Machine learning algorithms will enable predictive analytics, uncovering trends before they emerge.

3. Real-Time Data Collection

Advances in technology will allow real-time feedback from patients and healthcare providers.

4. Patient-Centric Research

Empowering patients to actively participate in research through digital platforms and mobile apps.

5. Globalization of Healthcare Studies

Cross-border research initiatives will become common, offering broader insights into global health challenges.

Conclusion

Quantitative market research in healthcare is a cornerstone of innovation and progress. By providing accurate, actionable data, it supports better patient care, enhances business strategies, and fosters advancements in medical technology. However, it requires meticulous planning, adherence to ethical standards, and the integration of cutting-edge tools to navigate its inherent challenges.

At Philomath Research, we specialize in delivering robust and reliable healthcare insights through quantitative research. Whether you’re a pharmaceutical company, healthcare provider, or policymaker, we can help you unlock the power of data to achieve your goals.

Contact us today to learn how our expertise can drive your healthcare research initiatives forward.

FAQs

1. What is quantitative market research in healthcare?

Quantitative market research in healthcare involves the collection and analysis of numerical data to understand consumer behaviors, measure service effectiveness, and predict trends. It focuses on structured methods like surveys, longitudinal studies, and data analytics to provide actionable insights that support decision-making in the healthcare sector.

2. Why is quantitative research important for the healthcare industry?

Quantitative research is essential in healthcare as it:

Facilitates data-driven decision-making for hospitals, pharmaceutical companies, and policymakers.

Enhances patient-centric care by understanding preferences and satisfaction.

Identifies emerging market trends and evaluates the effectiveness of treatments and technologies.

3. What are the primary methods used in quantitative healthcare research?

Common methods include:

Surveys and Questionnaires: For patient feedback and satisfaction metrics.

Longitudinal Studies: To observe changes over time, like chronic disease management.

Experimental Research: Controlled clinical trials to test drug efficacy.

Data Analytics and Secondary Research: Analysis of datasets like EHRs and government health reports.

4. How does quantitative research contribute to pharmaceutical advancements?

It helps pharmaceutical companies by:

Determining market size for new drugs.

Measuring the success of launched products.

Evaluating consumer perceptions regarding drug safety and effectiveness.

5. What role does quantitative research play in patient care improvement?

Quantitative research identifies gaps in care delivery, measures patient satisfaction, and tracks health outcomes, enabling healthcare providers to implement targeted improvements that enhance the overall patient experience.

6. What are the challenges of conducting quantitative research in healthcare?

Major challenges include:

Data Privacy Concerns: Adhering to regulations like HIPAA and GDPR.

Sampling Bias: Ensuring a diverse and representative sample.

Data Accuracy: Avoiding errors in self-reported data.

High Costs: Managing the expenses of large-scale studies or clinical trials.

Data Integration: Combining disparate data sources like EHRs and patient surveys.

7. How can researchers ensure reliable results in healthcare studies?

Researchers can ensure reliability by:

Defining clear objectives.

Using representative and inclusive sampling.

Leveraging technology like AI and machine learning.

Focusing on data security and ethical compliance.

Collaborating with experts for well-rounded insights.

8. What technologies are shaping the future of quantitative healthcare research?

Emerging technologies include:

Big Data Integration: Leveraging datasets from wearables, IoT, and EHRs.

AI-Driven Analytics: Machine learning for predictive insights and trend analysis.

Real-Time Data Collection: Tools for immediate patient feedback.

Patient-Centric Platforms: Empowering individuals to participate in research via apps.

9. What are the benefits of longitudinal studies in healthcare research?

Longitudinal studies provide in-depth insights into trends and patterns over time, such as medication adherence or disease progression, offering valuable data for long-term healthcare strategies.

10. How does Philomath Research support healthcare organizations in quantitative research?

Philomath Research delivers customized quantitative research solutions tailored to healthcare needs. We specialize in surveys, data analytics, and experimental studies, providing actionable insights that drive better decision-making for pharmaceutical companies, healthcare providers, and policymakers.

11. What are some real-world applications of quantitative healthcare research?

Key applications include:

Evaluating the effectiveness of public health campaigns.

Measuring patient satisfaction and experience.

Assessing the usability of healthcare technologies and devices.

Conducting competitor analysis and market positioning studies.

12. Why is data security important in healthcare research?

Data security is critical to protect sensitive patient information, maintain compliance with privacy regulations, and build trust with participants. Encryption, anonymization, and strict access controls are essential for safeguarding data integrity.

13. How does quantitative research help in healthcare policy-making?

Quantitative research supports policymakers by providing population-wide statistical data, evaluating the impact of public health campaigns, and identifying areas for policy intervention to improve overall healthcare outcomes.

[waghaditi]

Blood Collection Tubes Market: Exploring Future Trends and Growth Opportunities

The blood collection tubes market is rapidly evolving, driven by advancements in medical technologies and a growing emphasis on precision diagnostics. This market is pivotal for ensuring high-quality sample collection, a critical aspect of healthcare and research industries. Let’s explore the emerging trends shaping the future of this market.

1. Adoption of Smart Blood Collection Tubes

Integration of RFID technology for better sample tracking.

Enhanced compatibility with automated diagnostic systems.

Reduction in human error through digital data recording.

2. Rising Demand for Eco-Friendly Tubes

Development of biodegradable materials for tube manufacturing.

Minimization of biomedical waste with reusable blood collection systems.

Increased regulatory focus on sustainable practices in healthcare.

3. Expansion of Personalized Medicine

Blood collection tubes tailored for genomic and proteomic testing.

Increased use in cancer immunotherapy research.

Customization to meet the needs of targeted diagnostic solutions.

4. Growth in Home Diagnostic Testing

Tubes designed for safe and easy use by non-medical personnel.

Enhanced stability of samples during transportation.

Integration with home-testing kits to support telemedicine initiatives.

5. Advances in Material Science

Introduction of chemically inert polymers for extended sample preservation.

Enhanced resistance to chemical interactions and contamination.

Lightweight designs to improve handling and reduce shipping costs.

6. Focus on Patient Comfort and Safety

Development of tubes with reduced vacuum pressure for a gentler collection process.

Minimization of clotting and hemolysis risks with advanced additives.

Use of color-coded designs for easy identification and reduced errors.

7. Increasing Role of Point-of-Care Testing

Rise in demand for tubes optimized for immediate analysis.

Expansion of point-of-care testing in remote and rural areas.

Compatibility with portable diagnostic devices for rapid results.

8. Rise of AI-Driven Diagnostics

Blood collection tubes integrated with AI-based systems for predictive analysis.

Enhanced sample categorization using AI algorithms.

Faster and more accurate diagnostic outcomes with minimal manual intervention.

9. Surge in Chronic Disease Monitoring

Tubes designed for frequent monitoring of diseases like diabetes and cardiovascular disorders.

Better support for longitudinal studies and patient data tracking.

Improved additives to preserve biomarkers for extended periods.

10. Strengthening of Regulatory Frameworks

Development of standardized guidelines for blood collection tube manufacturing.

Improved compliance with international safety standards.

Enhanced quality assurance practices to meet regulatory demands.

11. Increasing Focus on Biobanking Applications

Growth in demand for tubes suitable for long-term sample storage.

Improved preservation methods for genetic and molecular research.

Tailored solutions for biobanking facilities in clinical and research settings.

12. Rising Investments in Emerging Markets

Expansion of healthcare infrastructure in developing regions.

Affordable tube options designed for low-income settings.

Enhanced awareness of diagnostic benefits among underserved populations.

13. Use of Wearable Technology for Collection Assistance

Integration of wearables to guide blood collection procedures.

Automated monitoring of blood volume and flow rates.

Real-time connectivity with diagnostic labs for immediate processing.

14. Enhanced Compatibility with Robotic Systems

Tubes designed for seamless use in robotic blood collection arms.

Improved efficiency and precision in automated systems.

Reduced reliance on manual intervention in high-volume laboratories.

15. Demand for Specialized Tubes for Research

Growth in research applications requiring niche tube formulations.

Tubes designed for rare sample types, such as plasma and serum studies.

Improved chemical stability for advanced clinical research needs.

16. Emergence of Disposable Capillary Tubes

Increasing preference for single-use capillary blood collection systems.

Reduction in cross-contamination risks in clinical environments.

Easy adoption in pediatric and geriatric diagnostic procedures.

17. Strengthening of Partnerships and Collaborations

Collaboration between manufacturers and diagnostic labs for product optimization.

Strategic partnerships for expansion into untapped markets.

Co-development of tubes for innovative diagnostic technologies.

18. Rising Popularity of Pre-Filled Additive Tubes

Pre-filled tubes with specific additives for tailored diagnostic needs.

Reduction in preparation time and enhanced workflow efficiency.

Increased demand from high-throughput testing facilities.

19. Technological Advancements in Vacuum Sealing

Improved vacuum technology for consistent sample volumes.

Extended shelf life of tubes with advanced sealing methods.

Greater reliability in diverse environmental conditions.

20. Growth of Global Supply Chains

Streamlined production and distribution processes for improved market reach.

Diversification of suppliers to mitigate risks and ensure availability.

Enhanced focus on localized production for faster delivery.

[phdrescue]

Top 5 Data Collection Methods for Research

Top 5 Data Collection Methods for Research

Selecting an appropriate data collection method is an important decision in any research project since it decides the quality and reliability of findings. Below are the top five methods, including their advantages, challenges, and applications, commonly used in research:

Surveys and Questionnaires: Surveys are probably the most popular data collection methods. The reasons are simple: it can be done on any scale or population with very few limitations. These tools are especially useful when gathering information from large groups of people, such as when conducted online, by phone, or face-to-face. Surveys may be constructed with closed questions for a quantitative analysis or with open questions to provide for qualitative input. However, the effectiveness of a survey depends on its design. Poorly worded questions or unclear instructions can lead to biased responses, making it crucial for researchers to pilot test their surveys before full deployment. Surveys are widely used in fields like market research, public health, and education to understand trends and opinions.

Interviews: This method involves one-on-one or small group interactions, enabling researchers to explore participants' experiences, attitudes, and perceptions in depth. Interviews can be designed in terms of predesigned questions, semi-structured ones, or unstructured interviews with flexibility to have free conversations. Though interviews provide detailed, rich data, it may be time-consuming and demanding skilled interviewers to avoid leading questions and misinterpretations. Transcribing and analyzing interview data also may take long time. However, interviews are inescapable in qualitative research such as in sociological studies and case studies.

Observation: In observational methods, behaviors, actions, or events are systematically recorded as they occur in natural or controlled settings. This method is useful when it is difficult to communicate about the phenomenon using verbal or written means. For instance, children's play behavior can be observed in a classroom or interactions at work. There may be either overt observation or covert observations. Here, the researcher maximizes his avoidance of the observer effect where participants can detect him or her. Subjective bias is also a limitation as a researcher might influence his description of the event. Often standardized protocols are used together with several observers.

Experiments: Experiments represent the ideal study where cause-and-effect relations are being considered. This means that in a controlled environment, manipulation of one or more variables will enable researchers to measure its effect on other variables. The experiment is particularly effective when used to test hypotheses in natural sciences, psychology, and the social sciences for cause-and-effect relationships. However, control over an experiment can sometimes result in limitations regarding its applicability in real situations. Moreover, ethical consideration plays a critical role, mainly if it deals with human participants. To ensure validity, researchers have to design experiments in a very careful manner with adequate controls and randomization.

Record and Record Review: Research based on the analysis of pre-existing records such as historical records, administrative data, or organizational reports offers insights without the need for contacting participants. This is a cost-effective method often used in archival research, policy analysis, and longitudinal studies. However, the quality of data depends upon the accuracy and completeness of original records. Further, in accessing some sensitive or proprietary information, the researcher may have some problem. To resolve such issues, they need to check on the reliability of sources and take necessary permission.

Each of these methods has its strengths and limitations, so their applicability depends on the research objectives, available resources, and ethical considerations. In many cases, the use of a combination of methods, known as mixed-methods research, can be very helpful in providing a deeper understanding of the research problem. Researchers can choose the best approach to achieve reliable and meaningful results by carefully weighing these factors.

For further research assistance reach out to us on our whatsapp https://wa.me/919424229851/

[chrisadovacy]

Harnessing the Power of Quantitative Data in Special Education

Sometimes, we need help to see the value of quantitative data in education support. We need to embrace its qualities.

Quantitative data is a cornerstone of evidence-based decision-making in Special Education. By providing measurable, objective insights into student performance, program effectiveness, and overall trends, quantitative data empowers educators and policymakers to craft impactful and accountable strategies.

Assessing Program Effectiveness Quantitative data, like test scores, attendance rates, and graduation rates, allows schools to evaluate the effectiveness of specific programs. For instance, if a school implements a new reading intervention, tracking improvements in literacy scores over time can objectively demonstrate the program’s success—or reveal areas for improvement.

Tracking Student Progress and Outcomes Data-driven insights help teachers understand how well students progress towards their Individualized Education Program (IEP) goals. Regular assessments of reading levels, math proficiency, and behaviour metrics provide concrete evidence of where a student is thriving and where additional support is needed. This enables educators to make timely adjustments, ensuring each student has the best possible chance of success.

Informing Policy and Resource Allocation Policymakers rely on quantitative data to understand the broader needs of special education programs. Data like funding impacts, staff-student ratios, and technology use help leaders allocate resources where needed, ensuring that schools have the support they need to serve students effectively. For instance, analyzing student-teacher ratios in special education classrooms can help allocate resources to optimize support.

Identifying Trends and Predicting Needs Longitudinal data—tracking trends over time—helps predict future needs. By examining patterns such as growth in the number of students requiring special education services or increasing rates of diagnoses, school districts can better prepare for changing demands. This forward-thinking approach is crucial for making informed decisions about staffing, curriculum, and facilities to meet the evolving needs of Special Education.

Supporting Inclusion and Equity Quantitative data highlights disparities in Special Education, revealing where improvements are needed to support inclusion and equity. For instance, if data shows a significant achievement gap between students with and without disabilities, it signals an area that needs targeted intervention. This awareness fosters accountability and prompts educators and policymakers to address disparities, promoting a more inclusive learning environment.

Quantitative data provides the hard evidence to support, improve, and advocate for Special Education. By systematically tracking progress, evaluating programs, and identifying gaps, educators and leaders are better equipped to drive meaningful change and ensure that all students have the opportunity to thrive.

References: ERIC. (2018). Quantitative Analysis in Educational Policy. Accessed from ERIC database. Alnaim, M. (2018). Quantitative Data in Special Education: Evaluating Interventions. Education Quarterly Reviews. International Journal of Social Learning. (2021). Data-Driven Decision Making in Inclusive Education.

[experiencesampling]

5 Reasons Why You Need EMA in Political Polling

Political polling has long been vital for understanding voter sentiment, forecasting election outcomes, and shaping campaign strategies. Phone interviews, online surveys, and face-to-face questionnaires have traditionally been used to gather voter opinions. However, these conventional approaches often have limitations, such as delayed responses, inaccurate recollection, and low participation rates.

This is where Ecological Momentary Assessment (EMA) comes in as an innovative, real-time data collection tool. Initially used in fields like psychology and healthcare, EMA has the potential to revolutionize political polling by offering more accurate, timely, and nuanced insights. This article will explore five key reasons political polling needs EMA.

Real-Time Data Collection Enhances Voter Insights

One of EMA's biggest advantages is its ability to capture voter sentiment at the moment rather than relying on the voters' recollection of their feelings or opinions days or weeks later. Traditional polling methods often ask respondents to reflect on past experiences or opinions, which can introduce bias or errors due to memory lapses.

EMA solves this problem by collecting data immediately after a political event, debate, advertisement, or even a candidate's social media post. This allows for more accurate and nuanced insights into voter reactions. For instance, voters can share their thoughts on a candidate's performance during a live debate in real time, giving pollsters a more detailed picture of how opinions shift from moment to moment.

By gathering this real-time data, EMA enhances the accuracy of polling results, leading to better-informed campaign strategies and political forecasting.

Context-Sensitive Polling Leads to More Accurate Results

In traditional polling methods, context—the situation or environment in which voters form their opinions—is often ignored. Voter opinions don't exist in a vacuum; they are shaped by a myriad of factors, such as location, mood, exposure to political ads, and even current events happening around them.

Ecological Momentary Assessment integrates data collection with real-world factors to account for context. For example, EMA could trigger a political survey when a voter is at a rally, watching a political ad, or even during a stressful commute. This context-sensitive approach allows pollsters to gather more accurate insights into how specific environments or stimuli affect voter behavior.

A voter's opinion while watching a political ad on television may differ from their opinion while reading news coverage on the same topic. EMA provides pollsters with a complete picture of how context influences voter sentiment—something traditional methods struggle to capture.

Higher Response Rates with Mobile-Based Polling

Getting people to participate in political polling has always been a challenge. In traditional phone and online surveys, declining response rates often lead to biased or incomplete results. EMA, however, thrives on the mobile-based, always-on nature of smartphones and wearable devices like the Apple Watch.

Because EMA collects data in real-time, participants can respond quickly and conveniently on their phones or wearables, wherever they are. This flexibility significantly boosts participation rates. Respondents are more likely to answer a quick question on their smartphone while waiting in line at the grocery store than to sit down and complete a long survey days after an event occurs.

Additionally, mobile-based EMA reduces non-response bias, common in traditional polling when specific groups (e.g., younger voters) are less likely to participate. By making polling easier and less intrusive, EMA can help ensure a broader, more representative sample.

Longitudinal Data Offers Deeper Trend Analysis

Political opinions are not static. They evolve, shaped by events, media coverage, personal experiences, and interactions. Ecological Momentary Assessment excels at gathering longitudinal data, meaning data collected repeatedly over time, allowing researchers to track how voter sentiment shifts and trends develop.

With EMA, pollsters can observe how opinions change after events like debates, political scandals, or economic news. Instead of relying on snapshots from a single point, longitudinal EMA data offers insights into long-term trends that help campaigns and analysts understand how opinions are shaped and which factors drive voting behavior.

For example, voters might initially feel neutral about a candidate but become more supportive as they watch positive campaign ads. Tracking these changes offers a more precise voter behavior prediction than traditional one-time surveys.

Captures a More Diverse and Representative Sample

One perennial challenge in political polling is ensuring a representative sample. Many traditional polling methods inadvertently exclude certain demographics, particularly younger, tech-savvy voters or people in rural or remote areas. This often results in polls that don't accurately reflect the population.

Ecological Momentary Assessment offers a solution by being highly accessible via smartphones and wearables. These devices are widely used across different demographics, meaning EMA can reach a broader, more diverse group of voters. By collecting data from people in various geographical areas, age groups, and socioeconomic backgrounds, EMA helps create a more representative sample, leading to more reliable polling outcomes.

The ability to capture data from underrepresented populations ensures that every voice is heard in the political process, which is essential for accurate polling and informed decision-making.

EMA for Political Polling

In the rapidly changing political landscape, real-time and context-aware insights have become crucial for understanding voter behavior. EMA offers political pollsters a cutting-edge tool to gather more accurate, diverse, and timely data, transforming how political survey  is conducted.

Ecological Momentary Assessment can revolutionize political polling by enhancing voter insights through real-time data collection, accounting for context, increasing response rates, tracking long-term trends, and capturing diverse samples. As we look toward future elections, adopting EMA in polling could provide a clearer and more detailed understanding of voter sentiment, helping campaigns and researchers stay ahead of the curve.

Now is the time for pollsters to embrace EMA and harness its capabilities to improve the accuracy and impact of political polling. Contact ExpiWell today to learn more about EMA for political polling. 

[evidence-based-activism]

Wdyt? https://x.com/statisticurban/status/1860817868251869345?s=46

This shows that although straight men commit the most crime gay men commit less crime than straight women.

So, that's not what that study [1] says.

The thin lines on each bar in the graph represent an error bar; they're present to visually indicate the degree of uncertainty in the estimate. In this case, the bars for heterosexual women, homosexual women, and homosexual men all overlap. Since the true rate for each group may be anywhere within this range, this study cannot be used to confirm this claim.

In particular, if we look at the charts showing the actual statistical tests (which help us decide if any observed effects are "true" or simply an artifact of this sample), we see gay boys only show lower rates of delinquency than gay girls for non-violent delinquency as adolescents and for violent delinquency as young adolescents. This is important since we already know sex differences in crime – while still present – are less dramatic for non-violent crime and for adolescent offenders.

Furthermore, the "non-violent delinquency" measure included questions like "acting rowdy", which, while inappropriate in some contexts, is not a criminal behavior. Further, a potential issue with their "violent delinquency" scale is that it fails to consider instigation/self-defense. For example, they include "getting into a physical fight", but do not appear to consider if the child in question instigated the fight or if they were defending themselves against another child(ren). Considering the data collection took place in 1994-1995, when overt homophobia was far more acceptable, I consider this a substantial issue with the design.

And as an additional important note, these researchers actually included bisexual individuals in both the heterosexual and homosexual categories, by collapsing across the "homosexual and mostly homosexual categories" and the "heterosexual and mostly heterosexual categories". Aside from the homophobia on display here, this also means their claims may not even reflect their data. (Without the data itself, I cannot determine if including bisexuals in these categories affected the results.)

---

So what about some accurate statistics?

This 2024 longitudinal, representative, national Dutch study [2] found that while "men in same-sex relationships were less likely to be a suspect of crime compared to those in opposite-sex relationships" and "women in same-sex relationships exhibited higher risk than those in opposite-sex unions", men (of any sexuality) were still more likely to commit crime than women (of any sexuality), particularly for violent crime.

Unfortunately, given the design, this study was still not able to determine the relationship with bisexuality (i.e., an unknown number of people in both homosexual and heterosexual relationships are actually bisexual).

This review and meta-analysis [3] on youth indicates "sexual minority girls, [but not boys], are disproportionally involved in the justice system", but cannot speak to the relative rates of gay girls to gay boys. And, again, what is considered "delinquent" in an adolescent does not always correspond to an actual crime (e.g., truancy).

This review [4] further expands on perpetration research in sexual minorities and highlights the substantial limitations in the research. In general, gay and bisexual youth perpetration varies broadly by what specific violence is being studied as well as study methodology. There is some limited support for the trend discussed above (i.e., perpetration rates decreasing from straight men -> gay men -> gay women -> straight women), but in general the limitations in the reviewed research cautioned against any firm findings. (To be clear, this review took place before [2] which is a strong study on this topic. Hopefully, more will now follow.)

Finally, the discussed trend isn't necessarily an effect of biology, as that poster appears to be implying. It's possible that gay women and men are conforming – in part – to the gendered expectations of the opposite sex (i.e., gender nonconformity, whether positive or negative in this case).

(But, also, as a note: much of the difference between women in same sex and opposite sex relationships appears to be driven by traffic offenses.)

I hope this helps!

References under the cut:

Beaver, K. M., Connolly, E. J., Schwartz, J. A., Boutwell, B. B., Barnes, J. C., & Nedelec, J. L. (2016). Sexual orientation and involvement in nonviolent and violent delinquent behaviors: Findings from the national longitudinal study of adolescent to adult health. Archives of Sexual Behavior, 45(7), 1759-1769.

van de Weijer, S. G., van Deuren, S., & Boutwell, B. B. (2024). Same-Sex Relationships and Criminal Behavior: A Total Population Study in The Netherlands. Archives of Sexual Behavior, 1-16.

Jonnson, M. R., Bird, B. M., Li, S. M., & Viljoen, J. L. (2019). The prevalence of sexual and gender minority youth in the justice system: A systematic review and meta-analysis. Criminal Justice and Behavior, 46(7), 999-1019.

McKay, T., Lindquist, C. H., & Misra, S. (2019). Understanding (and acting on) 20 years of research on violence and LGBTQ+ communities. Trauma, Violence, & Abuse, 20(5), 665-678.

[amrutatbrc1]

Road Profile Laser Sensor Market 2024-2033 : Demand, Trend, Segmentation, Forecast, Overview And Top Companies 

The Road Profile Laser Sensor Global Market Report 2024 by The Business Research Company provides market overview across 60+ geographies in the seven regions - Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa, encompassing 27 major global industries. The report presents a comprehensive analysis over a ten-year historic period (2010-2021) and extends its insights into a ten-year forecast period (2023-2033).

Learn More On The Road Profile Laser Sensor Market: https://www.thebusinessresearchcompany.com/report/road-profile-laser-sensor-global-market-report

According to The Business Research Company’s Road Profile Laser Sensor Global Market Report 2024, The road profile laser sensor market size is expected to see rapid growth in the next few years. It will grow to $1.11 billion in 2028 at a compound annual growth rate (CAGR) of 10.6%. The growth in the forecast period can be attributed to increasing demand for road safety solutions, increasing smart city initiatives, increasing infrastructure projects, rising focus on vehicle automation, increasing regulatory mandates promoting safer road conditions, and increasing demand for real-time road condition monitoring. Major trends in the forecast period include adoption in autonomous vehicles, advancements in high-resolution 3D mapping capabilities, integration with AI for real-time analytics, advancements in sensor technologies, and adoption of LiDAR technology for higher accuracy.

The rise in road accidents is expected to propel the growth of the road profile laser sensor market going forward. The surge in road accidents is due to more vehicles on the road, distracted driving habits, deteriorating road infrastructure, and weak enforcement of traffic regulations. Road profile laser sensors help reduce road accidents by accurately measuring and mapping road surface conditions, enabling timely detection and repair of hazards such as potholes, uneven surfaces, and debris. For instance, in May 2022, according to the National Highway Traffic Safety Administration, a US-based government agency focused on transportation safety, the number of fatalities from motor vehicle crashes surged by 10.5%, reaching an estimated 42,915 deaths in 2021, compared to 38,824 in 2020. Therefore, the rise in road accidents is driving the growth of the road profile laser sensor market.

Get A Free Sample Of The Report (Includes Graphs And Tables): https://www.thebusinessresearchcompany.com/sample.aspx?id=17247&type=smp

The road profile laser sensor market covered in this report is segmented –

1) By Measurement Range: Less Than 200 mm, 200–600 mm, More Than 600 mm 2) By Process: Biochemical Process, Thermochemical Process 3) By Application: Longitudinal Profile, Transverse Profile, Side Projections, Macro Texture, Other Applications

Major companies operating in the road profile laser sensor market are developing intelligent traffic system (ITS) solutions to develop efficient traffic management, enhance road safety, and enable smart city initiatives. Intelligent traffic system (ITS) solutions integrate road profile laser sensors with advanced data analytics, AI algorithms, and connectivity technologies to provide real-time insights into traffic flow, congestion monitoring, and pedestrian safety. For instance, in February 2021, Hikvision, a China-based digital technology company, launched the All-Rounder ITS camera, an innovative, intelligent traffic system (ITS) camera designed to improve road safety and optimize traffic flow. The All-Rounder ITS camera is ideal for various transportation scenarios, such as urban roads, highways, tunnels, and toll stations. The camera features an HD camera, speed radar, and light array integrated into a single housing. It provides stable and reliable performance, ensuring consistent operation in adverse weather and lighting conditions.

The road profile laser sensor market report table of contents includes:

1. Executive Summary

2. Road Profile Laser Sensor Market Characteristics

3. Road Profile Laser Sensor Market Trends And Strategies

4. Road Profile Laser Sensor Market - Macro Economic Scenario

5. Global Road Profile Laser Sensor Market Size and Growth ............

32. Global Road Profile Laser Sensor Market Competitive Benchmarking

33. Global Road Profile Laser Sensor Market Competitive Dashboard

34. Key Mergers And Acquisitions In The Road Profile Laser Sensor Market

35. Road Profile Laser Sensor Market Future Outlook and Potential Analysis

36. Appendix

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[inayaxx55]

Global Wet Tissues Market 2024 Key Players, Analysis, Share, Trends And Forecast To 2034

The Wet Tissues market report offered by Reports Intellect is meant to serve as a helpful means to evaluate the market together with an exhaustive scrutiny and crystal-clear statistics linked to this market. The report consists of the drivers and restraints of the Wet Tissues Market accompanied by their impact on the demand over the forecast period. Additionally, the report includes the study of prospects available in the market on a global level.

With tables and figures helping evaluate the Global Wet Tissues market, this research offers key statistics on the state of the industry and is a beneficial source of guidance and direction for companies and entities interested in the market. This report comes along with an additional Excel data-sheet suite taking quantitative data from all numeric forecasts offered in the study.

Get Sample PDF Brochure @ https://www.reportsintellect.com/sample-request/888999

Key players offered in the market: Kimberly-Clark,

APP,

Nice-Pak Products,

Procter & Gamble,

Albaad Massuot,

SCA,

Clorox,

Lenzing,

Rockline Industries

Additionally, it takes account of the prominent players of the Wet Tissues market with insights including market share, product specifications, key strategies, contact details, and company profiles. Similarly, the report involves the market computed CAGR of the market created on previous records regarding the market and existing market trends accompanied by future developments. It also divulges the future impact of enforcing regulations and policies on the expansion of the Wet Tissues Market.

Scope and Segmentation of the Wet Tissues Market

The estimates for all segments including type and application/end-user have been provided on a regional basis for the forecast period from 2024 to 2034. We have applied a mix of bottom-up and top-down methods for market estimation, analyzing the crucial regional markets, dynamics, and trends for numerous applications. Moreover, the fastest & slowest growing market segments are pointed out in the study to give out significant insights into each core element of the market.

Wet Tissues Market Type Coverage: - Cross Fold,

Longitudinal Fold.

Wet Tissues Market Application Coverage: - Baby,

Personal Care,

Cleaning,

Industrial

Regional Analysis:

North America Country (United States, Canada) South America Asia Country (China, Japan, India, Korea) Europe Country (Germany, UK, France, Italy) Other Countries (Middle East, Africa, GCC)

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The comprehensive report provides:

Complete assessment of all opportunities and threats in the global market.

Wet Tissues Market recent advancements and major events.

A thorough study of business policies for the growth of the Wet Tissues Market leading players.

Concluding study about the growth plot of Wet Tissues Market for upcoming years.

Detailed understanding of Wet Tissues Market particular drivers, restraints, and major micro markets.

Favorable impression inside vital technological and market latest trends hitting the Wet Tissues Market.

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What Is Statistical Analysis? Key Concepts with Examples

Statistical analysis is a powerful tool used to interpret and make sense of data, helping to uncover patterns, trends, and insights that are not immediately apparent. By applying statistical methods, researchers and analysts can draw meaningful conclusions and inform decision-making across various fields, from business and healthcare to social sciences and beyond. In this blog, we will explore what is Statistical Analysis, fundamental concepts of statistical analysis and provide practical examples to illustrate its application.

Understanding Statistical Analysis

At its core, statistical analysis involves collecting, reviewing, and interpreting data to make informed decisions. It combines mathematical theories and techniques to analyze numerical data and extract useful information. The process typically involves several key steps: data collection, data organization, data analysis, and interpretation.

Key Concepts in Statistical Analysis

Descriptive Statistics

Descriptive statistics summarize and describe the main features of a dataset. They provide a simple overview of the data, often through measures such as:

Mean: The average value of a dataset. For example, if the test scores of five students are 80, 85, 90, 95, and 100, the mean score is (80+85+90+95+100)/5 = 89.

Median: The middle value when the data is sorted in ascending or descending order. For the same test scores, the median is 90.

Mode: The most frequently occurring value in a dataset. If the scores were 80, 85, 90, 90, and 100, the mode would be 90.

Standard Deviation: A measure of the amount of variation or dispersion in a dataset. It indicates how much individual data points deviate from the mean.

Inferential Statistics

Inferential statistics allow us to make predictions or inferences about a population based on a sample of data. Common techniques include:

Hypothesis Testing: This involves making an assumption (the hypothesis) about a population parameter and then using statistical tests to determine if the sample data supports or rejects this assumption. For example, testing whether a new drug is more effective than an existing one involves setting up null and alternative hypotheses and analyzing clinical trial data.

Confidence Intervals: These provide a range of values within which we can be reasonably certain the true population parameter lies. For example, a confidence interval for the average height of a population might be 65-67 inches with 95% confidence.

Regression Analysis: This technique assesses the relationship between dependent and independent variables. For example, regression analysis can determine how factors like age, income, and education level affect an individual's spending behavior.

Probability

Probability is a fundamental concept in statistics that measures the likelihood of an event occurring. It is used to make predictions and assess risk. For instance:

Basic Probability: If a die is rolled, the probability of getting a six is 1/6, as there is one favorable outcome out of six possible outcomes.

Conditional Probability: This measures the probability of an event occurring given that another event has already occurred. For example, if a card is drawn from a deck and it is known to be a spade, the probability of it being a queen is 1/13.

Correlation and Causation

Correlation: This measures the strength and direction of the relationship between two variables. A positive correlation means that as one variable increases, the other does too, while a negative correlation indicates that as one variable increases, the other decreases. For example, there might be a positive correlation between hours studied and exam scores.

Causation: Unlike correlation, causation implies that one variable directly affects another. Establishing causation typically requires experimental or longitudinal studies. For instance, a well-designed experiment might show that increasing exercise leads to improved cardiovascular health.

Examples of Statistical Analysis in Action

Business: A company might use statistical analysis to evaluate customer satisfaction surveys. By analyzing the responses, the company can identify key areas for improvement, measure the effectiveness of changes made, and predict customer retention rates.

Healthcare: Researchers can apply statistical analysis to clinical trials to assess the effectiveness of new treatments. By comparing the health outcomes of patients receiving the treatment versus a control group, they can determine whether the new treatment is beneficial.

Social Sciences: Statisticians in social sciences might analyze survey data to understand public opinion on various issues. For example, they might use regression analysis to explore how demographic factors influence voting behavior.

Conclusion

Statistical analysis is an essential tool for understanding and interpreting data across various domains. By mastering key concepts such as descriptive and inferential statistics, probability, and the distinction between correlation and causation, individuals can make more informed decisions and derive actionable insights from data. Whether in business, healthcare, or social research, statistical analysis provides a framework for making sense of complex data and addressing real-world questions with precision and confidence.