Student Mental Health Survey

Key aspects covered in the dataset include:

• Demographic details such as gender, age, and university.
• Academic details like degree level, major, academic year, and current CGPA.
• Student's residential status and experiences with discrimination, harassment, or bullying on campus.
• Student's lifestyle factors include frequency of sports engagement and average sleep hours per night.
• Student's satisfaction with their field of study and their perception of academic workload.
• Addressing the academic pressure, financial concerns, and the quality of social relationships on campus.
• Frequency of experiencing depression, anxiety, feelings of isolation, and insecurity about the future.
• Activities that students engage in to relieve stress.

This dataset is valuable for researchers, and policymakers interested in student well-being, mental health, and academic success.

This dataset is created by combining surveys from the last five years (2017-2021) conducted by Open Sourcing Mental Illness (a non-profit corporation) to determine the presence of mental health issues among those employed in the technology sector and to measure attitudes regarding mental health in the workplace.

Survey responses in the dataset are filtered based on having a tech role as primary criteria and descriptive questions are removed besides checking for data consistency and validity of survey responses to make analysis possible.

OSMI has made the data sets from the 2017 to 2021 survey available on Kaggle.

sflagg/Kaggle-Mental-Health-Survey-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

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

The New York City Community Mental Health Survey (CMHS) was a one-time telephone survey conducted by the DOHMH. The CMHS was conducted in conjunction with the annual 2012 Community health Survey (CHS). The CMHS provides robust data on the mental health of New Yorkers, including neighborhood, borough, and citywide estimates. The data are analyzed and disseminated to influence mental health program decisions, and increase the understanding of the mental health among New Yorkers.

📘 Dataset Description

This dataset provides a realistic, synthetic simulation of global mental health survey responses from 10,000 individuals. It was created to reflect actual patterns seen in workplace mental health data while ensuring full anonymity and privacy.

🧠 Context & Purpose

Mental health issues affect people across all ages, countries, and industries. Understanding patterns in mental health at work, access to treatment, and stigma around disclosure is essential for shaping better workplace policies and interventions.

This dataset is ideal for:

• Training and evaluating machine learning models
• Practicing classification or clustering techniques
• Performing exploratory data analysis (EDA)
• Studying fairness and bias in mental health predictions
• Creating realistic dashboards for HR analytics or healthcare systems

📊 Dataset Highlights

• 10,000 rows representing anonymized individuals
• Diverse global coverage with country/state info
• Demographic attributes like age, gender, employment type
• Information about work environment and company support
• Responses about mental health history, treatment, and workplace stigma

💡 Example Use Cases

• Predicting the likelihood of an employee seeking mental health treatment
• Identifying factors most correlated with workplace stress
• Segmenting users by mental health risk using clustering
• Building fairness-aware models to reduce bias in mental health predictions

⚠️ Notes

• This dataset is entirely synthetic. No personally identifiable information (PII) or real user data is included.
• It was generated based on patterns observed in public mental health datasets and surveys.

This dataset was designed as part of a thesis project on mental health prediction. The dataset includes synthetic demographic and psychological variables. Data were generated using Python code to simulate realistic patterns and scoring based on psychological constructs.This dataset was to develop a machine learning model for predicting severity of multiple mental health disorders & overall Mental Health Status based on survey responses and psychological features.

Data source description - Adults: NHIS monitors the health of the U.S. population by collecting and analyzing data on a broad range of health topics. Interviews are conducted continuously throughout the year, and are initiated in-person, with telephone follow-up. NHIS focuses on the health of children and adults in the United States. One adult household member is randomly selected to be the subject of a detailed health interview. If children are present, one child is also randomly selected. Adults answer on their own behalf, while a knowledgeable adult answers on behalf of the selected child. NHIS topics featured include adult life satisfaction, anxiety, depression, mental health conditions, mental health care, and social and emotional support.

Data source description - Teenagers: NHIS-Teen was a web-based health survey of teenagers between the ages of 12 to 17. Answers from teenagers helped paint a picture of the health of teenagers living in the United States. NHIS-Teen covered questions on a variety of health topics, including doctor visits, mental health, and social and emotional support. Data were collected between July 2021 and December 2023. 

For additional information, please see: https://www.cdc.gov/mental-health/about-data/mental-health-data-sources.html" target ="_blank">Mental Health Data Sources.

This an annual report that presents findings from the 2012 National Mental Health Services Survey (N-MHSS) conducted from September 2012 through February 2013. The N-MHSS collects information from all the known facilities in the United States, both public and private, that provide mental health treatment services to people with mental illness. The N-MHSS is designed to collect data on the location, characteristics, and utilization of organized mental health treatment service providers throughout the 50 states, the District of Columbia, and the U.S. territories.

ADHD

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

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 Wellness & Screen Time Survey (400 Users)

This dataset captures insights from 400 survey participants on how their daily screen usage relates to mental wellness. With the growing prevalence of digital devices in our lives, understanding the link between screen time, sleep quality, stress, and productivity is a crucial research area for data science, psychology, and public health.

Dataset Overview

Each row represents a unique participant and includes:

Demographics (age, gender, occupation, student/working)

Daily Screen Time (mobile, laptop, TV, total)

Sleep Quality (self-reported rating)

Stress Levels (scale 1–10)

Productivity Score (self-perception)

Mental Wellness Indicators (mood, energy, focus)

Possible Explorations

Relationship between screen hours vs stress levels

Does more screen time affect sleep quality?

Screen usage patterns among students vs professionals

Predicting mental wellness scores from lifestyle variables

Clustering user groups based on digital habits

Why This Dataset?

Kaggle users often explore topics around digital life and health. This dataset is designed to be approachable for beginners (EDA, visualization, correlations) yet deep enough for advanced users (clustering, regression, predictive modeling).

Potential Analyses

Correlation heatmaps (screen hours vs wellness factors)

Regression models to predict sleep quality or stress

Clustering users into "healthy balance" vs "high risk" digital habits

Sentiment/word cloud analysis if extended with text data

👉 Use this dataset to explore how technology affects our daily lives and mental well-being.

The National Mental Health Services Survey (N-MHSS) is an annual survey designed to collect statistical information on the numbers and characteristics of all known mental health treatment facilities within the 50 States, the District of Columbia, and the U.S. territories. In every other year, beginning in 2014, the survey also collects statistical information on the numbers and demographic characteristics of persons served in these treatment facilities as of a specified survey reference date. The N-MHSS is the only source of national and State-level data on the mental health service delivery system reported by both publicly-operated and privately-operated specialty mental health treatment facilities, including: public psychiatric hospitals; private psychiatric hospitals, non-federal general hospitals with separate psychiatric units; U.S. Department of Veterans Affairs medical centers; residential treatment centers for children; residential treatment centers for adults; outpatient or day treatment or partial hospitalization mental health facilities; and multi-setting (non-hospital) mental health facilities. The N-MHSS complements the information collected through SAMHSA's survey of substance abuse treatment facilities, the National Survey of Substance Abuse Treatment Services (N-SSATS). Treatment facility Information from the N-MHSS is used to populate the mental health component of SAMHSA's online Behavioral Health Treatment Services Locator.

The Collaborative Psychiatric Epidemiology Surveys (CPES) were initiated in recognition of the need for contemporary, comprehensive epidemiological data regarding the distributions, correlates and risk factors of mental disorders among the general population with special emphasis on minority groups. The primary objective of the CPES was to collect data about the prevalence of mental disorders, impairments associated with these disorders, and their treatment patterns from representative samples of majority and minority adult populations in the United States. Secondary goals were to obtain information about language use and ethnic disparities, support systems, discrimination and assimilation, in order to examine whether and how closely various mental health disorders are linked to social and cultural issues. To this end, CPES joins together three nationally representative surveys: the NATIONAL COMORBIDITY SURVEY REPLICATION (NCS-R), the NATIONAL SURVEY OF AMERICAN LIFE (NSAL), and the NATIONAL LATINO AND ASIAN AMERICAN STUDY (NLAAS). These surveys collectively provide the first national data with sufficient power to investigate cultural and ethnic influences on mental disorders. In this manner, CPES permits analysts to approach analysis of the combined dataset as though it were a single, nationally representative survey. Each of the CPES surveys has been documented in a comprehensive and flexible manner that promotes cross-survey linking of key data and scientific constructs.

This is the second (wave 2) 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 February/March 2021, during the Coronavirus (COVID-19) pandemic and changes since 2017. Experiences of family life, education, and services during the COVID-19 pandemic are also examined. The sample for the Mental Health Survey for Children and Young People, 2021 (MHCYP 2021), wave 2 follow up was based on 3,667 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 three primary aims: Aim 1: Comparing mental health between 2017 and 2021 – 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 - Topic 2 examines the circumstances and experiences of children and young people in February/March 2021 and the preceding months, covering: COVID-19 infection and symptoms. Feelings about social media use. Family connectedness. Family functioning. Education, including missed days of schooling, access to resources, and support for those with Special Educational Needs and Disabilities (SEND). Changes in circumstances. How lockdown and restrictions have affected children and young people’s lives. Seeking help for mental health concerns. Aim 3: Present more detailed data on the mental health, circumstances and experiences of children and young people by ethnic group during the coronavirus pandemic (where sample sizes allow). The data is broken down by gender and age bands of 6 to 10 year olds and 11 to 16 year olds for all categories, and 17 to 22 years old for certain categories where a time series is available, 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.

This survey screened for a range of mental health conditions, including common mental health conditions (using the CIS-R), attention deficit hyperactivity disorder (ADHD, ASRS), posttraumatic stress disorder (PTSD, PCL-C), signs of dependence on drugs and alcohol (AUDIT), gambling harms (PGSI), personality disorder (SAPAS, SCID-II Q) and bipolar disorder (MDQ). Clinical examinations assessed autism (ADOS), psychotic disorders (SCAN) and eating disorders (SCAN ED). See the relevant chapters for further details on each condition or health behaviour and how it was examined.

These data sets are survey results collected by OSMI and are made available by the CC-BY-SA 4.0 license. One survey was performed in 2014 and the other in 2016. Both of the surveys seek to understand how people that work in technology view mental health issues and to understand what support they receive from their employer.OSMI has made the data sets for both the 2014 survey and the 2016 survey available on Kaggle.

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.

This report presents findings from the 2018 National Mental Health Services Survey (N-MHSS), an annual census of all known facilities in the United States, both public and private, that provide mental health treatment services to people with mental illness. Planned and directed by the Center for Behavioral Health Statistics and Quality (CBHSQ) of the Substance Abuse and Mental Health Services Administration (SAMHSA), U.S. Department of Health and Human Services, the N-MHSS is designed to collect data on the location, characteristics, and utilization of organized mental health treatment services for facilities within the scope of the survey throughout the 50 states, the District of Columbia, Puerto Rico, and other jurisdictions.