ABSTRACT OBJECTIVE Evaluating characteristics of unpaid domestic work and its association with mental disorders, exploring gender differences. METHODS We analyzed cross-sectional data from the second wave of an urban population cohort (n = 2,841) aged 15 and older from a medium-sized city in Bahia (BA). The representative population sample was randomly selected in subsequent multiple steps. We interviewed the survey participants at their homes. This study analyzed sociodemographic, occupational, unpaid domestic work and mental illness data, stratified by sex (gender). We investigated the association between the work-family-personal time conflict, the effort-reward imbalance in domestic and family work and the occurrence of common mental disorders, such as generalized anxiety disorder and depression. We estimated prevalence, prevalence ratios and their respective 95% confidence intervals. RESULTS Among the participants, the unpaid domestic activities were performed by 71.3% of men and 95.2% of women, who were responsible for the investigated activities, except for minor repairs. The percentages of paid work were higher among men (68.1% versus 47.2% among women). The distribution of stressors and conflict experiences showed an inverse situation between genders: men depicted the highest high percentage of low work-family-personal time conflict (39.0%), while among women, the highest percentage was of high conflict (40.0%); 45.8% of the men reported low effort-reward imbalance in domestic and family work, while only 28.8% of women reported low imbalance. The investigated mental disorders were more prevalent among women, who showed a significant association between work-family-personal time conflict and common mental disorders, as well as depression; among men, conflict was positively associated with common mental disorders. The effort-reward imbalance, in turn, was strongly related to CMD (Common Mental Disorders), generalized anxiety disorder and depression among women. Amid men, this discrepancy was only associated to depression. CONCLUSIONS Domestic work persists as a mostly feminine assigned activity. The stressful situations of unpaid domestic work and the work-family-personal time conflict were more strongly associated with adverse effects on the female mental health.

In the United States, the prevalence of mental illness in the past year is more common among females than males and more common among the young than the old. As of 2024, some 26.7 percent of females reported some type of mental illness in the past year, compared to 20 percent of males. Common forms of mental illness include depression, anxiety disorders, and mood disorders. Depression Depression is one of the most common mental illnesses in the United States. Depression is defined by prolonged feelings of sadness, hopelessness, and despair leading to a loss of interest in activities once enjoyed, a loss of energy, trouble sleeping, and thoughts of death or suicide. It is estimated that around five percent of the U.S. population suffers from depression. Depression is more common among women with around six percent of women suffering from depression compared to four percent of men. Mental illness and substance abuse Data has shown that those who suffer from mental illness are more likely to suffer from substance abuse than those without mental illness. Those with mental illness are more likely to use illicit drugs such as heroin and cocaine, and to abuse prescription drugs than those without mental illness. As of 2023, around 7.9 percent of adults in the United States suffered from co-occuring mental illness and substance use disorder.

Percentage of persons aged 15 years and over by perceived mental health, by gender, for Canada, regions and provinces.

For a summary of the case study, please go to "Portfolio Project".

Context

This data analysis was meant to show that men have their own issues in society that are being ignored. The mental health has been declining especially for men. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. This data analysis was meant to show that men have their own issues in society that are being ignored. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. These variables may require a separate dataset going into more detail about them.

A space dedicated just for men and another just for women to speak about their problems with help and constructive criticism for growth and for social belonging maybe required to improve the mental health of society (among other variables). This does not mean that the struggles of women are nonexistent. There are already a multitude of datasets and articles dedicated to some of the possible struggles of women from MSNBC, CNN, NBC, BBC, Netflix movies, and even popular secular music like recent songs WAP from Megan Thee Stallion, God is a Women by Arianna Grande, etc. This dataset's objective was not made to continue to light a flame between the already hostile relationships that modern men and women have with each other. Awareness without bias is the goal.

For the results, please read the portfolio project and leave comments.

Content

Where the data were obtained:

1. The first excel file was obtained from https://data.world/vizzup/mental-health-depression-disorder-data/workspace/file?filename=Mental+health+Depression+disorder+Data.xlsx

2. The second excel file was obtained from https://ourworldindata.org/grapher/male-vs-female-suicide

3. The third excel file was obtained from https://ourworldindata.org/suicide

4. The fourth excel file was obtained from https://ourworldindata.org/drug-use

Inspiration

I want to be the best data analyst ever, so criticism (regardless of the harshness), it will be greatly appreciated. What would you have added/improved on? Was it easy to understand? What else do you want me to make a dataset on?

Explore differences between male and female young adults for: any mental illness, serious mental illness, major depressive episodes, mental health treatment, and suicidal behavior. Estimates were based on data from the 2021 National Survey on Drug Use and Health (NSDUH).

As of October 2021, women had the highest share of mental health disorders in India, amounting to ** percent and ** percent for stress and anxiety health disorder respectively. Comparatively, ** percent of men had depression as compared to women with ** percent during the same time period.

This table contains 93984 series, with data for years 2002 - 2002 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Newfoundland and Labrador; Nova Scotia ...), Age group (4 items: 65 years and over;25 to 64 years;15 to 24 years; Total; 15 years and over ...), Sex (3 items: Both sexes; Females; Males ...), Mental health and well-being profile (89 items: Total population for the variable major depressive episode; Major depressive episode; all measured criteria are met; Major depressive episode; measured criteria not met; Major depressive episode; not stated ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons; High 95% confidence interval; number of persons ...).

A student depression dataset typically contains data aimed at analyzing, understanding, and predicting depression levels among students. It may include features such as demographic information (age, gender), academic performance (grades, attendance), lifestyle habits (sleep patterns, exercise, social activities), mental health history, and responses to standardized depression scales.

These datasets are valuable for research in psychology, data science, and education to identify factors contributing to student depression and to design early intervention strategies. Ethical considerations like privacy, informed consent, and anonymization of data are crucial in working with such sensitive information.

1. File Structure
2. Format: CSV format
3. Rows: Each row represents an individual student.
4. Columns: Each column represents a specific feature or attribute.

1. Columns
2. ID: Unique identifier for each student.
3. Age: Age of the student.
4. Gender: Gender (e.g., Male, Female).
5. City: Geographic region
6. CGPA: Grade Point Average or other academic scores.
7. Sleep Duration: Average daily sleep duration.
8. Profession:
9. Work Pressure:
10. Academic Pressure:
11. Study Satisfaction:
12. Job Satisfaction:
13. Dietary Habits: And much more

1. Target Variable
2. Depression_Status: Binary (Yes/No)

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

According to the World Health Organisation, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labour-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis. Participants: (1) full brain 128-electrodes EEG experiment: 53 participants include a total of 24 outpatients (13 males and 11 females; 18–55-year-old) diagnosed with depression, as well as 29 healthy controls (20 males and 9 females; 18–55-year-old) were recruited; (2) pervasive 3-electrodes EEG experiment: 55 participants include a total of 26 outpatients (15 males and 11 females; 18–55-year-old) diagnosed with depression, as well as 29 healthy controls (19 males and 10 females; 18–55-year-old) were recruited; (3) Audio experiment: 52 participants include a total of 23 outpatients (16 males and 7 females; 18–55-year-old) diagnosed with depression, as well as 29 healthy controls (20 males and 9 females; 18–55-year-old) were recruited. For more information please see the Methodology file.

This table contains 14784 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...), Age group (14 items: Total; 12 years and over; 15-19 years; 12-19 years; 12-14 years ...), Sex (3 items: Both sexes; Males; Females ...), Contact with health professionals about mental health (4 items: Total population for the variable contact with health professionals about mental health; Contact with health professionals about mental health; not stated; Contact with health professionals about mental health in past 12 months; No contact with health professionals about mental health in past 12 months ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

BackgroundHaoxinqing, China’s largest online mental health platform, facilitates digital psychological care delivery. This study aims to describe the demographics and medical service data of doctors on the Haoxinqing platform and investigate their associations.MethodThe study analyzed the demographic information and medical service data of 11,333 registered physician users on the Haoxinqing platform over a 5-year period.ResultAmong registered physicians, 87.0% were from secondary or tertiary hospitals and were concentrated in eastern provinces (e.g., Guangdong: 918). Female physicians had a lower proportion in senior titles (chief physicians: 19.0% vs. 20.0% for males), although the chi-square analysis indicated a weak association between gender and professional title (Cramer’s V = 0.051, P < 0.001). Text and image consultations dominate (82.1%). Professional titles significantly impacted service volume: chief physicians had 3.85 times more patients (IRR = 3.85, 95% CI [2.11–7.00]) and prescribed 4.16 times more medications (IRR = 4.16, 95% CI [3.21–5.41]) than residents (P < 0.001). Negative binomial regression showed that male physicians had 30% fewer patients than females (IRR = 0.70, 95% CI [0.58–0.85], P < 0.001), but the effect size for the association between gender and consultation methods was low (Cramer’s V = 0.036).ConclusionBased on cross-sectional data from China’s largest online mental health platform, this study revealed that online services, while supplementing offline medical care, are still influenced by traditional medical hierarchy. Patients’ trust in senior physicians and gendered communication norms are critical determinants affecting resource allocation patterns on digital platforms.

This is the underlying data from a 2022 evaluation of a group-based intervention that draws on affirmative cognitive behavioral therapy (CBT) strategies for men who have sex with men (MSM) and transgender women (TGW) at risk for or living with HIV in Lagos, Nigeria. The data set comprises four (4) rounds of survey data in this delayed intervention group randomized controlled trial: (1) baseline (immediate and delayed group), (2) post (immediate group), (3) post (delayed group), and (4) three-month follow-up (immediate group only). The intervention consisted of four weekly in-person group sessions each 2.5-3 hours in length, facilitated by community health workers. There were 240 participants in trial, which was supported by the Elton John AIDS Foundation.

The goal of this research project was to develop an efficient mental health screen that would aid in the early identification of severe mental illnesses and other acute psychiatric problems during the jail intake process. The researchers sought to validate the Brief Jail Mental Health Screen (BJMHS) as such a tool. Participants in the study included male and female jail detainees admitted to one of four county jails, two in Maryland and two in New York, from November 2005 to June 2006. A total of 10,562 jail detainees were screened using the BJMHS-R (Part 1). The screening data were used to identify a sub-sample of detainees who were systematically sampled for a detailed clinical assessment, the Structured Clinical Interview for DSM-IV (SCID), which was conducted by a trained research interviewer in order to validate the screen. A subset of 464 jail detainees completed the SCID interviews (Part 2). Part 1, Tracking Data, contains 54 variables, including items and scores from the BJMHS-R, that were used to used to identify and generate a list of potential detainee participants for the SCID interview. Part 2, Interview Data, contains 326 variables, including items and scores from both the BJMHS-R and the SCID interviews, that were used to validate the screen.

Number of cases of common mental health problems per 1000 of the population aged 16-74 by type of mental health problem.

Data on rates are presented as total cases per 1000 population aged 16-74.

Detail of case numbers are available from NEPHO for males and females, for quinary age-groups. These are not intended to be used at this level, rather to provide flexible data for grouping up.

This dataset contains responses from students regarding their mental health status during the era of online learning. The data was collected via surveys and focuses on various psychological and behavioral aspects influenced by remote education.

The dataset can be used for exploratory data analysis (EDA), data visualization, and predictive modeling to better understand how online education impacts student mental well-being.

The dataset consists of 1,000 entries and 10 columns, covering demographic details, lifestyle habits, and self-reported mental health indicators. Here's a summary of the columns and their purposes:

| Name Student's first name (non-essential for analysis; can be anonymized)

| Gender Gender of the respondent (Male/Female)

| Age Age in years

| Education Level Academic level (e.g., Class 8, BTech, MSc)

| Screen Time (hrs/day) Average screen time per day during online learning

| Sleep Duration (hrs) Average daily sleep duration

|**Physical Activity (hrs/week)** Weekly exercise time

| Stress Level Reported stress level (Low, Medium, High)

| Anxious Before Exams Whether the student feels anxious before exams (Yes/No)

| Academic Performance Change Self-assessed change in academic performance

Mind-body internet- and mobile-based intervention (IMIs) are gaining traction as scalable and effective strategies to manage mental health symptoms experienced by people living with chronic physical conditions. Sex and gender have implications for mind-body IMI participation, adherence, and efficacy. The objective of this secondary analysis was to assess the extent and nature of reporting of sex and/or gender in randomized controlled trials retrieved by a primary systematic review of mind-body IMIs assessing depression and anxiety symptoms among adults living with chronic physical conditions. The collected information included whether sex and gender-based analyses were carried out and explored the role of sex and gender on mental health outcomes, attrition, and recruitment rates. The protocol was registered with PROSPERO. A comprehensive search of six electronic databases was completed from database inception to March 2023. Sex and gender terms were summarized according to a standardized, three-point criteria: (1) non-binary use (i.e., > 2 categories used for both sex and gender definitions) (2) use of appropriate categories (i.e., sex = male/female/intersex, gender = man/woman/gender-diverse) and (3) non-interchangeable use of sex or gender terms throughout the citation. The use of sex and gender terms was deemed correct if all three criteria were met. The role of sex and gender on mental health outcomes, attrition and recruitment data were extracted where available. In the 56 included studies, 7691 participants were evaluated with a mean age of 43 years and 4780 (62%) were described as females/women. Two (4%) studies defined sex or gender using non-binary categorization. Twenty-eight (50%) studies used appropriate categories to define sex or gender. Twenty-five (45%) studies used sex and gender terms non-interchangeably. No studies met all three sex/gender criteria. Only one study provided stratified mental health scores by sex and/or gender within the publication. Eleven (20%) studies reported sex or gender imbalance as being a potential reason for outcome differences, with 3 studies conducting an adjusted statistical analysis investigating sex/gender as a moderator. Findings highlight low uptake of sex and gender considerations in the context of mind-body IMIs. Results underscore the need to incorporate guideline-based sex and gender terms and concepts, from data collection and analysis to reporting of evidence to inform mind-body IMI development and guide future research. Stratified sex and/or gender analyses are encouraged in future studies to assess intervention outcome differences.

This table contains 84672 series, with data for years 2005 - 2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Age group (14 items: Total; 12 years and over; 12 to 19 years; 15 to 19 years; 12 to 14 years ...) Sex (3 items: Both sexes; Males; Females ...) Contact with health professionals about mental health (4 items: Total population for the variable contact with health professionals about mental health; No contact with health professionals about mental health in the past 12 months; Contact with health professionals about mental health in the past 12 months; not stated; Contact with health professionals about mental health in the past 12 months ...) Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; High 95% confidence interval; number of persons; Low 95% confidence interval; number of persons ...).

This table contains 142464 series, with data for years 2003 - 2003 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (106 items: Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 15 to 19 years; 12 to 19 years; 12 to 14 years ...) Sex (3 items: Both sexes; Females; Males ...) Contact with health professionals about mental health (4 items: Total population for the variable contact with health professionals about mental health; No contact with health professionals about mental health in past 12 months; Contact with health professionals about mental health in past 12 months; not stated; Contact with health professionals about mental health in past 12 months ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

Data is based on a phone survey of 1,545 rural Indian households collected in August 2020 in 20 districts across 6 states (Rajasthan, Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh, and Maharashtra) in Northern India in August 2020. Households participated in a 20–30 min survey with two parts, a household head module and a female respondent module. In the household head module the household head surveyed about the household’s socioeconomic conditions, household head’s income, the male and female heads’ nutrition, and the number of days the respondent wished for more food for themselves or their children. The nutrition questions were taken from the National Family and Health Survey (NFHS) 2015–16, allowing to use the pre-pandemic responses to the survey from the same district to benchmark nutritional outcomes. After the head module, if the head was male, the head was asked to pass the phone to a female household member (typically the female household head). The female responded to an additional survey asking about her mental health and status within the household, as well as if this had changed since the pandemic. In cases where the respondent to the head module was female, the same respondent answered the female survey. Altogether, this allowed the female module to be conducted with 573 women. To ascertain information on women’s mental health, a selection of questions from the PHQ9 depression diagnostic scale and the GAD7 anxiety scale was asked. For a subset of questions, respondents’ were asked if outcomes have changed due to the pandemic. For example, for each of the mental health questions above (as well as the safety question), respondents were asked a follow-up question about whether their experiences have improved, worsened, or stayed the same since the pandemic. Measuring changes in these outcomes, enables to both assess the aggregate effects of the pandemic and measure the relationship between lockdowns and outcome variables, accounting for pre-pandemic differences across individuals. Additional data on case rates/deaths The phone survey data were supplemented with additional district level data on COVID-19 cases and deaths between the start of the pandemic and the time of the survey. Also hospitalization data from HMIS were used.