This dataset contains related to anxiety, depression, and mental health influences. It includes demographic details, lifestyle habits, mental health indicators, medical history, coping mechanisms, and stress factors. The dataset is designed for mental health analysis, predictive modeling, and research on the impact of various factors on mental well-being.

Features Included: Demographics: Age, Gender, Education, Employment Status

Lifestyle Factors: Sleep Hours, Physical Activity, Social Support

Mental Health Metrics: Anxiety Score, Depression Score, Stress Level

Medical History: Family History of Mental Illness, Chronic Illnesses, Medication Use

Coping Strategies: Therapy, Meditation, Substance Use

Additional Factors: Financial Stress, Work Stress, Self-Esteem, Life Satisfaction, Loneliness

Age

Gender

Education_Level

Employment_Status

Sleep_Hours

Physical_Activity_Hrs

Social_Support_Score

Anxiety_Score

Depression_Score

Stress_Level

Family_History_Mental_Illness

Chronic_Illnesses

Medication_Use

Therapy

Meditation

Substance_Use

Financial_Stress

Work_Stress

Self_Esteem_Score

Life_Satisfaction_Score

Loneliness_Score

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison.County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities.Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

Introduction: Population health concerns have been raised about negative impacts from overuse of digital technologies. We examine patterns of online activity predictive of Digital Overuse and Addictive Traits (DOAT). We explore associations between DOAT and mental well-being and analyse how both relate to self-reported changes in self-esteem, perceived isolation, and anxiety about health when individuals use the internet for health purposes.Methods: A cross-sectional nationally representative household survey of adults using stratified random sampling (compliance 75.4%, n = 1,252). DOAT was measured using self-reported questions adapted from a social media addiction scale (failure to cut down use, restlessness when not using, and impact on job/studies and home/social life in the last year), combined into a single DOAT score. Higher DOAT score was defined as >1 standard deviation above population mean. The Short Warwick-Edinburgh Mental Well-being Scale was used to measure mental well-being. Analyses were limited to those with internet access (n = 1,003).Results: Negative impacts of digital technology use on work and home/social lives were reported by 7.4% of respondents. 21.2% had tried but failed to cut down use in the past year. Higher DOAT was associated with higher social media and internet use but also independently associated with greater risks of low mental well-being. Higher DOAT was associated with both improvement and worsening of self-esteem, perceived isolation and anxiety about health when using the internet for health reasons, with no change in these outcomes most likely in those with lower DOAT. Lower mental well-being was associated with a similar bi-directional impact on perceived isolation and was also associated with worsening self-esteem.Conclusions: Substantial proportions of individuals report negative impacts on home, social and working lives from digital technology use, with many trying but failing to cut down use. Individuals with higher DOAT may experience improvements or worsening in self-esteem and other measures of mental well-being when using the internet for health purposes. From a public health perspective, a greater understanding of risk factors for digital overuse, its impacts on well-being and how to reasonably limit use of technology are critical for a successful digital revolution.

The objective is to investigate the prevalence of sleep quality and mental disturbances of medical staff and identify the effects of region, epidemic stage, and demographic characteristics during the COVID-19 outbreak in China.
Self-administered questionnaire were sent to health care workers (HCWs) in China from 30 Jan to 2 March, 2020. The Pittsburgh Sleep Quality Index, the Patient Health Questionare-9, the Generalized Anxiety Disorder-7 and the Impact of Event Scale were used to assess sleep quality, depression symptoms, anxiety symptoms and Post-traumatic stress disorder (PTSD) of HCWs, respectively. The influencing factors of psychological and sleep disturbances were identified by univariate analysis and multiple regression. Research found that HCWs had poorer sleep quality on stage 2 and 3 of the outbreak. HCWs in Hubei had poorer sleep quality but lighter depression condition. gender, age, occupation and status of having children were associated with sleep and mental health. Mental health programs should be considered for HCWs especially those with specific characteristics.

This Synthetic Remote Work Mental Health Dataset is created for educational and research purposes in organizational psychology, mental health, and data science. It provides demographic, occupational, and mental health-related details of individuals working in various job roles and industries under remote or onsite work arrangements. The dataset enables analysis of work-life balance, stress, mental health conditions, and organizational support in remote work settings.

Dataset Features

• Age: Age of the individual in years.
• Gender: Gender of the individual (Male/Female/Prefer not to say).
• Job_Role: Job title or primary role of the individual (e.g., Marketing, Sales, Designer).
• Industry: Industry sector where the individual is employed (e.g., Finance, Education, Retail).
• Years_of_Experience: Total years of professional work experience.
• Work_Location: Current work setting (Remote/Onsite).
• Hours_Worked_Per_Week: Average number of hours worked per week.
• Number_of_Virtual_Meetings: Number of virtual meetings attended weekly.
• Work_Life_Balance_Rating: Self-reported rating of work-life balance (1 = Poor, 5 = Excellent).
• Stress_Level: Stress level of the individual (Low/Medium/High).
• Mental_Health_Condition: Presence of a diagnosed mental health condition (e.g., Anxiety, Depression, Burnout).
• Access_to_Mental_Health_Resources: Whether the individual has access to mental health resources at work (Yes/No).
• Productivity_Change: Change in productivity level due to remote or onsite work (Increase/Decrease/No Change).
• Social_Isolation_Rating: Rating of perceived social isolation (1 = Low, 5 = High).
• Satisfaction_with_Remote_Work: Satisfaction with remote work arrangements (1 = Unsatisfied, 5 = Highly Satisfied).
• Company_Support_for_Remote_Work: Frequency of company-provided support for remote work (None/Weekly/Daily).
• Physical_Activity: Level of physical activity reported (None/Weekly/Daily).
• Sleep_Quality: Self-reported quality of sleep (Poor/Average/Good).
• Region: Geographic region where the individual resides (e.g., Europe, South America, Asia).

Distribution

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Usage

This dataset is suited for the following applications:

• Mental Health Research: Analyze relationships between stress levels, mental health conditions, and organizational support in remote or onsite work settings.
• Productivity Analysis: Explore how remote work affects productivity based on work-life balance, virtual meetings, and stress levels.
• Organizational Policy Design: Develop data-driven workplace policies to support employees' mental health and satisfaction.
• Social Isolation Studies: Investigate the impact of remote work on social connectedness and isolation.
• Health and Wellness Promotion: Examine the role of physical activity, sleep quality, and access to mental health resources in employee well-being. ### Coverage This synthetic dataset is anonymized and adheres to data privacy standards. It is designed for research and learning purposes, representing diverse demographics, industries, and work settings.

License

CC0 (Public Domain)

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to mental health and workplace dynamics.
• Psychologists and Researchers: To explore trends in workplace mental health and employee well-being.
• Human Resources Professionals: To design evidence-based interventions for improving work-life balance and employee satisfaction.
• Public Health Analysts: To study the effects of remote work on mental health at a population level.
• Policy Makers and Regulators: For data-driven decision-making to promote mental health and productivity in remote or hybrid workplaces.

The “Mental Health Impact of COVID-19 Pandemic on NIMH Patients and Volunteers” study was a longitudinal study launched in spring 2020 by researchers at NIMH, to investigate the effect of the emerging COVID-19 pandemic on mental health. For each participant, the study collected personal characteristics, such as demographics, psychological traits, and clinical history, together with personal circumstances at regular intervals during their enrollment in the study.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

The impact of the coronavirus 2019 (COVID-19) pandemic on different countries and populations is well documented in quantitative studies, with some studies showing stable mental health symptoms and others showing fluctuating symptoms. However, the reasons behind why some symptoms are stable and others change are under-explored, which in turn makes identifying the types of support needed by participants themselves challenging. To address these gaps, this study thematically analysed 925 qualitative responses from five open-ended responses collected in the UCL-Penn Global COVID Study between 17 April to 31 July 2021 (wave 3). Three key themes comprised of 13 codes were reported by participants across countries and ages regarding the impact of COVID-19 on their health, both mental and physical, and livelihoods. These include: 1) Outlook on self/life, 2) Self-improvement, and 3) Loved ones (friends and family). In terms of support, while 2.91% did not require additional support, 91% wanted support beyond financial. Other unexpected new themes were also discussed regarding vulnerable populations suffering disproportionately. The pandemic has brought into sharp focus various changes in people’s mental health, physical health, and relationships. Greater policy considerations should be given to supporting citizens’ continued access to mental health when considering pandemic recovery.

According to a survey conducted between April and May 2023 among TikTok users in the United States, **** percent of Generation Z users reported feeling that TikTok was addictive. In comparison, around ** percent of Gen X respondents felt the same. Millennials were the demographic reporting the highest number of TikTok users feeling negative mental health effects of the platform, with **** percent of respondents reporting having experienced such effects.

According to a March 2024 survey conducted in the United States, 32 percent of adults reported feeling that social media had neither a positive nor negative effect on their own mental health. Only seven percent of social media users said that online platforms had a very positive effect on their mental health, while 12 percent of users said it had a very negative impact. Furthermore, 22 percent of respondents said social media had a somewhat negative effect on their mental health. Is social media addictive? A 2023 survey of individuals between 11 and 59 years old in the United States found that over 73 percent of TikTok users agreed that the platform was addictive. Furthermore, nearly 27 percent of those surveyed reported experiencing negative psychological effects related to TikTok use. Users belonging to Generation Z were the most likely to say that TikTok is addictive, yet millennials felt the negative effects of using the app more so than Gen Z. In the U.S., it is also not uncommon for social media users to take breaks from using online platforms, and as of March 2024, over a third of adults in the country had done so. Following mental health-related content Although online users may be aware of the negative and addictive aspects of social media, it is also a useful tool for finding supportive content. In a global survey conducted in 2023, 32 percent of social media users followed therapists and mental health professionals on social media. Overall, 24 percent of respondents said that they followed people on social media if they had the same condition as they did. Between January 2020 and March 2023, British actress and model Cara Delevingne was the celebrity mental health activist with the highest growth in searches tying her name to the topic.

Dataset including the data analysed in the "r/RoastMe: Characterising Self-Requested Online Mocking" paper describing the demographics, motivations, mental-health issues and consequences of posting on the r/RoastMe subreddit.

In 2023, over 20 percent of 25 to 39 year olds in Europe stated recent world events (such as the COVID-19 pandemic, the conflict in Ukrain, climate crisis, unemployment, food and energy costs rising) influenced their mental health to a great extent. In general, mental health across all demographics had been influenced to some extent negatively by world events.

This is the first in a series of follow up reports to the Mental Health and Young People Survey (MHCYP) 2017, exploring the mental health of children and young people in July 2020, during the Coronavirus (COVID-19) pandemic and changes since 2017. Experiences of family life, education and services, and worries and anxieties during the COVID-19 pandemic are also examined. The sample for the Mental Health Survey for Children and Young People, 2020 (MHCYP 2020), wave 1 follow up was based on 3,570 children and young people who took part in the MHCYP 2017 survey, with both surveys also drawing on information collected from parents. Cross-sectional analyses are presented, addressing two primary aims: Aim 1: Comparing mental health between 2017 and 2020 – the likelihood of a mental disorder has been assessed against completion of the Strengths and Difficulties Questionnaire (SDQ) in both years in Topic 1 by various demographics. Aim 2: Describing life during the COVID-19 pandemic - the report examines the circumstances and experiences of children and young people in July 2020 and the preceding months, covering: Family dynamics (Topic 2) Parent and child anxieties about COVID-19, and well-being (Topic 3) Access to education and health services (Topic 4) Changes in circumstances and activities (Topic 5) The data is broken down by gender and age bands of 5 to 10 year olds and 11 to 16 year olds for all categories, and 17 to 22 years old for certain categories, as well as by whether a child is unlikely to have a mental health disorder, possibly has a mental health disorder and probably has a mental health disorder. This study was funded by the Department of Health and Social Care, commissioned by NHS Digital, and carried out by the Office for National Statistics, the National Centre for Social Research, University of Cambridge and University of Exeter. Note: On 21 December 2020 the pdf was amended to ensure that Figure 5.6 was displaying the correct figures from the underlying data table.

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

This dataset includes data from the Young and Well Towns (YAWT) Collaborative Research Centre (CRC) project. An uncontrolled trial was conducted that investigated the use and effect of mobile apps for mental health and wellbeing in young people. The study targeted adolescents and young adults (age 16 - 25) from Australia. Participants were asked to complete a profiling survey that assessed demographic characteristics, mental health, personality, and app use. Furthermore, they were asked to use and link a range of freely and commercially available health, fitness, or wellbeing apps. A range of app-specific metrics were assessed throughout the study period. Individuals were asked to use the mobile apps for a period of at least two weeks. Participants were continuously monitored over the study period with regard to subjective mood, sleep, rest and energy, through regular web-based self-report assessments.Date coverage: 2016-06-01 - 2017-01-31

A poll released to mark World Mental Health Day reveals that during the 12 months preceding the survey, 15% of respondents across EU Member States sought professional help for psychological or emotional problems and 7% took antidepressants, mostly for depression or anxiety. According to the results, there is still stigma attached to mental disorders, with 22% of those surveyed saying they would find it difficult to speak to a person with a "significant mental disorder". This issue and the other results will be discussed during the next thematic conference under the European Pact for Mental Health and Well-being. The main themes addressed in this report are: • The state of mental well-being – how well people feel mentally and physically, and what impact has this had on their lives• Level of comfort at work – how secure people feel in their current jobs, whether they feel their skills match their current role and whether they feel they receive adequate recognition/respect for what they do • Care and treatment – what help and treatment people have sought to ameliorate any mental health conditions they have experienced • Perceptions of people with mental illness – how comfortable people feel about interacting with those with a mental health problem

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

This report presents findings from the third (wave 3) in a series of follow up reports to the 2017 Mental Health of Children and Young People (MHCYP) survey, conducted in 2022. The sample includes 2,866 of the children and young people who took part in the MHCYP 2017 survey. The mental health of children and young people aged 7 to 24 years living in England in 2022 is examined, as well as their household circumstances, and their experiences of education, employment and services and of life in their families and communities. Comparisons are made with 2017, 2020 (wave 1) and 2021 (wave 2), where possible, to monitor changes over time.

Mental Health in Drug Users During COVID-19

Exploring Personality and Risk Profiles

By [source]

About this dataset

This anonymous online survey dataset explores the mental health outcomes of psychedelic and non-psychedelic drug users during the COVID-19 pandemic. Using psychometric scales to assess personality traits, anxiety, negative and positive affect, well-being and resilience, principal component analysis was applied to ascertain drug use reports from the sample population. Risk profiles including risk taking/avoidance behaviours, risk perception and risk tolerance are analysed to gain a deeper insight into potential correlations with mental health outcomes. Investigating these factors reveals information on how different psychosocial factors may impact on an individual’s wellbeing in times of uncertainty as experienced in this global pandemic

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How to use the dataset

This dataset contains survey responses from psychedelic and non-psychedelic drug users during the COVID-19 pandemic, exploring their mental health outcomes and personality traits. It is intended for researchers to use for studying correlations between drug use and risk profile factors with mental health outcomes in order to better understand the effects of different substances on individuals’ psychological wellbeing during this unprecedented global event.

To help you get started working with this dataset, we have provided a description of the data columns below. 0_1: Age (Numeric) 0_2: Gender (Categorical) 0_3: Education Level (Categorical) 0_4: Employment Status (Categorical) **0_5: Country of Residence (Categorical) **0_6: Number of Psychedelic Drug Experiences (Numeric) **0_7 :Number of Non-psychedelic Drug Experiences(Numeric) [This field is blank if zero]

** 0_8 :Personality Traits(Numeric)[ This field is blank if zero]

** 0_9 :Anxiety( Numeric )[ This field is blank if zero]

** 0 _ 10: Negative Affect( Numeric )[ This field is blank if zero ]

** 0 _ 11 : Positive Affect( Numeric )[ This field is blank if zero ]

** 0 _ 12 : Well - being( Numeric )[ This field is blankifzero ]

\( 0013\)\({ m }\)Resilience\({ m }\)left({ m numeric} ight){ m [This~~field~~is~~blankifzero]}\(\)\({15} {16} {17} {18} {19}\)\({ m Risk~Tolerance~~~~~Risk~Aversion~~~~~Risk~Taking~~~~~~~~~~~~Risk~Avoidance~~~~Mental}{20}{21}{22}{23}{24}{25{26}}{27 { f Health ~Outcomes}}} \)left({ m numeric} ight)mathop {{}}limits^{^{approx }}_{_. } {kern 1pts } {kern 1pts }left({{{20}, 21, 22, 23, 24, 26}, 27} ight)cong ({695

Research Ideas

• Metaregression analysis exploring the impact of psychedelic and non-psychedelic drug use on mental health outcomes, such as anxiety and negative affect.
• Create machine learning models that can be deployed to assess the potential risk for developing mental health issues based on personality traits, substance use, risk profiles and demographics.
• Analyzing trends in psychedelic/non-psychedelic drug usage during the COVID-19 pandemic through a visual dashboard display for policy makers and mental health professionals to better identify at-risk groups of individuals

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: ocean157ksafe.csv | Column name | Description | |:----------------------------------|:----------------------------------------------------------------------------------------------| | 0_1 | Age of the respondent. (Numeric) | | 0_2 | Gender of the respondent. (Categorical) | | 0_3 | Country of residence of the respondent. (Categorical) | | **0_4*...

In partnership with the Florida Department of Corrections (FDC), this study a) collected prison administrative data to create person-level cohort-analysis files of inmates admitted to and released from Florida prisons between July 1, 2007 and December 31, 2015, b) collected stock population data of inmates incarcerated on June 30, 2011, c) examined recidivism outcomes, d) examined the effects of long-term solitary confinement on inmate behavior and mental health, and e) conducted a survey of prison personnel from November 4, 2019 to January 10, 2020, in order to conduct an analysis to address the need in public policy decision-making for evidence on the impacts of restricted housing on inmates, prisons and personnel, and public safety overall.