This dataset appears to contain a variety of features related to text analysis, sentiment analysis, and psychological indicators, likely derived from posts or text data. Some features include readability indices such as Automated Readability Index (ARI), Coleman Liau Index, and Flesch-Kincaid Grade Level, as well as sentiment analysis scores like sentiment compound, negative, neutral, and positive scores. Additionally, there are features related to psychological aspects such as economic stress, isolation, substance use, and domestic stress. The dataset seems to cover a wide range of linguistic, psychological, and behavioural attributes, potentially suitable for analyzing mental health-related topics in online communities or text data.

Benefits of using this dataset:

• Insight into Mental Health: The dataset provides valuable insights into mental health by analyzing linguistic patterns, sentiment, and psychological indicators in text data. Researchers and data scientists can gain a better understanding of how mental health issues manifest in online communication.
• Predictive Modeling: With a wide range of features, including sentiment analysis scores and psychological indicators, the dataset offers opportunities for developing predictive models to identify or predict mental health outcomes based on textual data. This can be useful for early intervention and support.
• Community Engagement: Mental health is a topic of increasing importance, and this dataset can foster community engagement on platforms like Kaggle. Data enthusiasts, researchers, and mental health professionals can collaborate to analyze the data and develop solutions to address mental health challenges.
• Data-driven Insights: By analyzing the dataset, users can uncover correlations and patterns between linguistic features, sentiment, and mental health indicators. These insights can inform interventions, policies, and support systems aimed at promoting mental well-being.
• Educational Resource: The dataset can serve as a valuable educational resource for teaching and learning about mental health analytics, sentiment analysis, and text mining techniques. It provides a real-world dataset for students and practitioners to apply data science skills in a meaningful context.

New York State is moving Medicaid behavioral health services from a fee-for-service system into Managed Care. OMH Medicaid Behavioral Health Measures were developed to help monitor the transition of mental health and substance use disorder services from a fee-for-service to behavioral managed care. The MH Readmission dataset displays percentages of Medicaid discharges for members 6-64 years of age who were hospitalized in an inpatient setting with a primary diagnosis of mental illness that were followed by an unplanned Mental Health readmission within 30 and 90 days of discharge.

Mental Health reports the prevalence of the mental illness in the past year by age range.

This publication contains the official statistics about uses of the Mental Health Act(1) ('the Act') in England during 2018-19. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. However, some providers that make use of the Act are not yet submitting data to the MHSDS, or submitting incomplete data. Improvements in data quality have been made over the past year. NHS Digital is working with partners to ensure that all providers are submitting complete data and this publication includes guidance on interpreting these statistics. Footnotes (1) The Mental Health Act 1983 as amended by the Mental Health Act 2007 and other legislation.

VAMC-level statistics on the prevalence, mental health utilization, non-mental health utilization, mental health workload, and psychological testing of Veterans with a possible or confirmed diagnosis of mental illness. Information prepared by the VA Northeast Program Evaluation Center (NEPEC) for fiscal year 2015. This dataset is no longer supported and is provided as-is. Any historical knowledge regarding meta data or it's creation is no longer available. All known information is proved as part of this data set.

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.

New York State is moving Medicaid behavioral health services from a fee-for-service system into Managed Care. OMH Medicaid Behavioral Health Measures were developed to help monitor the transition of mental health services from a fee-for-service to behavioral managed care. The MH Ambulatory Follow-up dataset displays percentages of Medicaid discharges for members 6-64 years of age who were hospitalized in an inpatient setting with a primary diagnosis of mental illness that were followed by an outpatient visit, an intensive outpatient encounter or partial hospitalization for mental health treatment within 7 and 30 days of discharge.

A novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users.

Advances in artificial intelligence (AI) in general and Natural Language Processing (NLP) in particular are paving the new way forward for the automated detection and prediction of mental health disorders among the population. Recent research in this area has prioritized predictive accuracy over model interpretability by relying on deep learning methods. However, prioritizing predictive accuracy over model interpretability can result in a lack of transparency in the decision-making process, which is critical in sensitive applications such as healthcare. There is thus a growing need for explainable AI (XAI) approaches to psychiatric diagnosis and prediction. The main aim of this work is to address a gap by conducting a systematic investigation of XAI approaches in the realm of automatic detection of mental disorders from language behavior leveraging textual data from social media. In pursuit of this aim, we perform extensive experiments to evaluate the balance between accuracy and interpretability across predictive mental health models. More specifically, we build BiLSTM models trained on a comprehensive set of human-interpretable features, encompassing syntactic complexity, lexical sophistication, readability, cohesion, stylistics, as well as topics and sentiment/emotions derived from lexicon-based dictionaries to capture multiple dimensions of language production. We conduct extensive feature ablation experiments to determine the most informative feature groups associated with specific mental health conditions. We juxtapose the performance of these models against a “black-box” domain-specific pretrained transformer adapted for mental health applications. To enhance the interpretability of the transformers models, we utilize a multi-task fusion learning framework infusing information from two relevant domains (emotion and personality traits). Moreover, we employ two distinct explanation techniques: the local interpretable model-agnostic explanations (LIME) method and a model-specific self-explaining method (AGRAD). These methods allow us to discern the specific categories of words that the information-infused models rely on when generating predictions. Our proposed approaches are evaluated on two public English benchmark datasets, subsuming five mental health conditions (attention-deficit/hyperactivity disorder, anxiety, bipolar disorder, depression and psychological stress).

Citation

Rani, S.; Ahmed, K.; Subramani, S. From Posts to Knowledge: Annotating a Pandemic-Era Reddit Dataset to Navigate Mental Health Narratives. Appl. Sci. 2024, 14, 1547. https://doi.org/10.3390/app14041547

RMHD Our dataset, meticulously curated from Reddit, encompasses a comprehensive collection of posts from five key subreddits focused on mental health: r/anxiety, r/depression, r/mentalhealth, r/suicidewatch, and r/lonely. These subreddits were chosen for their rich, focused discussions on mental health issues, making them invaluable for research in this area.

The dataset spans from January 2019 through August 2022 and is systematically structured into folders by year. Within each yearly folder, the data is further segmented into monthly batches. Each month's data is compiled into five separate CSV files, corresponding to the selected subreddits.

Structure of Part A : Raw Data:Each CSV file in our dataset includes the following columns, providing a detailed view of the Reddit posts along with essential metadata: Author: The username of the Reddit post's author. Created_utc: The UTC timestamp of when the post was created. Score:The net score (upvotes minus downvotes) of the post. Selftext: The main text content of the post. **Subreddit: **The subreddit from which the post was sourced. Title: The title of the Reddit post. Timestamp:The local date and time when the post was created, converted from the UTC timestamp. This structured approach allows researchers to conduct detailed, time-based analyses and to easily access data from specific subreddits.

Structure of Part B : Labelled Data :Part B of our dataset, which includes a subset of 800 manually annotated posts, is structured differently to provide focused insights into the mental health discussions. The columns in Part B are as follows: Score: The net score (upvotes minus downvotes) of the post. Selftext:The main text content of the post. Subreddit: The subreddit from which the post was sourced. Title: The title of the Reddit post. Label: The assigned label indicating the identified root cause of mental health issues, based on our annotation process are : Drug and Alcohol , Early Life, Personality,Trauma and Stress

This annotation process brings additional depth to the dataset, allowing researchers to explore the underlying factors contributing to mental health issues.

The dataset, with a zipped size of approximately 1.68GB, is publicly available and serves as a rich resource for researchers interested in exploring the root causes of mental health issues as represented in social media discussions, particularly within the diverse conversations found on Reddit.

This data set includes monthly counts and rates (per 1,000 beneficiaries) of behavioral health services, including emergency department services, inpatient services, intensive outpatient/partial hospitalizations, outpatient services, or services delivered through telehealth, provided to Medicaid and CHIP beneficiaries, by state. Users can filter by either mental health disorder or substance use disorder. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating behavioral health services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

Background: With the control of the epidemic, adolescents' mental outlook might have improved. However, little evidence existed with regard to the psychological status of adolescents in post-COVID-19 era. This present study aimed to explore the psychological status of high school students after the epidemic getting eased.Methods: A web-based cross-sectional survey was used to obtain data from three high schools, including the demographic information, the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder-7 (GAD-7), the Self-Rating Scale of Sleep (SRSS), and self-designed general recent-status questionnaire. Correlation analysis was performed to explore potential associations between the depression symptoms, anxiety symptoms, and sleep status. The PHQ-9 and GAD-7 differences between nowadays data and the data enrolled 12 months before were also compared.Result: A total of 1,108 qualified questionnaires were obtained. The prevalence of depressive and anxious symptoms was 27.5 and 21.3%, respectively, from mild to severe in all students, while 11.8% of these high students got sleep disturbances. Both the rate and the severity of depression, anxiety and sleep problems of female students were higher than male students. Grade three students suffered higher prevalence and severer mental disturbances than the other two grades. There were significant correlations between the depression symptoms, anxiety symptoms, and sleep status. The psychological status has been improved in nowadays high school students compared with the sample enrolled 12 months before.Conclusion: As a supplement to our former study, this present research provided a perspective on the psychological status of high school students 1 year after the COVID-19 pandemic being well controlled. We should pay attention to the psychological status of high school students, and should also notice the progresses made by this special group after the epidemic.

This publication provides the most timely statistics available relating to NHS funded secondary mental health, learning disabilities and autism services in England. This information will be of use to people needing access to information quickly for operational decision making and other purposes. These statistics are derived from submissions made using version 3.0 of the Mental Health Services Dataset (MHSDS). This edition includes final statistics for October 2018 and provisional statistics for November 2018. NHS Digital review the quality and completeness of the submissions used to create these statistics on an ongoing basis. More information about this work can be found in the Accuracy and reliability section of this report. Fully detailed information on the quality and completeness of particular statistics in this release is not available due to the timescales involved in reviewing submissions and engaging with data providers. The information that has been obtained at the time of publication is made available in the Provider Feedback sections of the Data Quality Reports which accompany this release. Information gathered after publication is released in future editions of this publication series. More detailed information on the quality and completeness of these statistics and a summary of how these statistics may be interpreted is made available later in our Mental Health Bulletin: Annual Report publication series. All elements of this publication, other editions of this publication series, and related annual publication series' can be found in the Related Links below. We are aware that a number of providers may have encountered issues submitting mental health data to the Bureau Service Portal (BSP) in December due to a power outage; the issue may have affected the quality of the data the provider was able to submit or prevented the provider from submitting at all. The full impact on the data is being investigated with some information available from the data quality reports. Included for the first time this month are four new measures: CCR70b - New Emergency Referrals to Crisis Care teams in RP, Aged under 18; CCR71b - New Urgent Referrals to Crisis Care teams in RP, Aged under 18; CCR72b - New Emergency Referrals to Crisis Care teams in RP, with first face to face contact, Aged under 18 and CCR73b - New Urgent Referrals to Crisis Care teams in RP, with first face to face contact, Aged under 18. Please note: The provider breakdown for AMH04 (People in contact with adult mental health services on CPA at the end of RP with HoNOS recorded) has not been included in this publication and will not be included in future publications until the cause is rectified. NHS Digital will inform users once this issue has been resolved. NHS Digital apologises for any inconvenience caused.

We collect this dataset from some mental health-related subreddits in https://www.reddit.com/ to further the study of mental disorders and suicidal ideation. We name this dataset as Reddit SuicideWatch and Mental Health Collection, or SWMH for short, where discussions comprise suicide-related intention and mental disorders like depression, anxiety, and bipolar. We use the Reddit official API and develop a web spider to collect the targeted forums. This collection contains a total of 54,412 posts. Specific subreddits are listed in Table 4 of the below paper, as well as the number and the percentage of posts collected in the train-val-test split.

This dataset is only for research. Please request with your institutional email.

If you use this dataset, please cite the paper as:

Ji, S., Li, X., Huang, Z. et al. Suicidal ideation and mental disorder detection with attentive relation networks. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-06208-y

@article{ji2021suicidal,
 title={Suicidal ideation and mental disorder detection with attentive relation networks},
 author={Ji, Shaoxiong and Li, Xue and Huang, Zi and Cambria, Erik},
 journal={Neural Computing and Applications},
 year={2021},
 publisher={Springer}
}

Objectives: To identify the prevalence and factors associated with common mental disorders in adult women.Methods: Searches were carried out in the PubMed, Web of Science, Science Direct, Scopus, Cinahl, Google Scholar and Open Gray databases. The study protocol was registered with PROSPERO under number CRD42020168231. Cross-sectional studies showing the prevalence of common mental disorders in women over 18 years were included. Studies with men, children and pregnant women of another age group and with other mental disorders and other types of studies were excluded. The Joanna Briggs Institute checklist was used to assess the risk of bias.Results: Nineteen studies were included in this review. The prevalence of CMD ranged from 9.6% to 69.3%. The main associated factors were unemployment, indebtedness, low income, being a housewife, smoking, low education, poor self-rated health, being single, divorced or widowed. The risk of bias in the studies was classified as low and moderate.Conclusion: This review revealed a variable prevalence rate of CMD in adult women. Public policies are needed to create strategies to prevent the mental illness of these women.

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.

Research in assessing the Mental Health Problems (MHPs), e.g., stress, anxiety, and depression of university students has had much interest worldwide for the last decade. This article provides a large and comprehensive dataset concerning the MHPs of 2028 students from 15 top-ranked universities in Bangladesh, including 9 government/public universities and 6 private universities. To collect the data, the GAD-7 (for Anxiety), PSS-10 (for Stress), and PHQ-9 (for Depression) models are adopted to reflect equivalent academic perspectives. Additionally, student sociodemographic data are collected. The adoption of these three models are done by a team of five professors and a student psychologist to best capture the academic and socio-demographic factors that influence MHPs among university students. To conduct the survey, a google form is developed and circulated among the 15 faculty representatives from the participating universities who further circulated and conducted the survey with the students. Collected data is evaluated to ensure the sufficiency of sample size, and internal consistency and reliability of the response. Furthermore, the levels of anxiety, stress, and depression are calculated using the data to demonstrate its' applicability. This dataset can be used to measure the trajectory of students’ the mental and psychosocial stressors, to adopt required mental health and counselling services, and to conduct data intensive Machine Learning (ML) model development to predictive MPH assessment.

This dataset is part of the ESCALA (Study of Urban Health and Climate Change in Informal Settlements in Latin America) project that was funded by the Lacuna Fund of the Meridian Institute https://lacunafund.org/. This dataset contains aggregated counts of mental health services by age, sex, year, service type, and diagnosis for Bogota, Colombia, 2019-2023. Data were provided by the RIPS (Spanish acronym for "Individual Records of Service Provision") and consolidated from SISPRO (Spanish acronym for "Comprehensive Social Protection Information System") - Ministry of Health and Social Protection. The data were organized and published on the portal saludata.saludcapital.gov.co and openly published on datosabiertos.bogota.gov.co. Each row in the database represents the count of care services, not the count of unique individuals served. Therefore, it is not possible to calculate the total number of individuals served by summing the partial values obtained at different levels of disaggregation. This is because the same person may be included in different groups within the same period if any of their attributes change over time. Data cleaning included: (1) Initially, two databases are obtained: one covering the period from 2019 to 2021 and another from 2022 to 2024 (up to August). First, both databases are unified, retaining only the columns they have in common. (2) Since the data are not disaggregated by month, the 2024 records are removed as they provide an unofficial count for all months, which could lead to errors during use. (3) Empty rows and reports outside Bogotá are removed. (4) Finally, each variable is adjusted by assigning the names established in the data dictionary and categorizing them according to the defined domains.

Objectives: Define the role of increasing cannabis availability on population mental health (MH).

Methods. Ecological cohort study of National Survey of Drug Use and Health (NSDUH) geographically-linked substate-shapefiles 2010-2012 and 2014-2016 supplemented by five-year US American Community Survey. Drugs: cigarettes, alcohol abuse, last-month cannabis use and last-year cocaine use. MH: any mental illness, major depressive illness, serious mental illness and suicidal thinking. Data analysis: two-stage and geotemporospatial methods in R.

Results: 410,138 NSDUH respondents. Average response rate 76.7%. When all drug exposure, ethnicity and income variables were combined in final geospatiotemporal models tobacco, alcohol cannabis exposure, and various ethnicities were significantly related to all four major mental health outcomes. Cannabis exposure alone was related to any mental illness (β-estimate= -3.315+0.374, P<2.2x10-16), major depressive episode (β-estimate= -3.712+0.454, P=3.0x10-16), serious mental illness (SMI, β-estimate= -3.063+0.504, P=1.2x10-9), suicidal ideation (β-estimate= -3.013+0.436, P=4.8x10-12) and with more significant interactions in each case (from β-estimate= 1.844+0.277, P=3.0x10-11). Geospatial modelling showed a monotonic upward trajectory of SMI which doubled (3.62% to 7.06%) as cannabis use increased. Extrapolated to whole populations cannabis decriminalization (4.35+0.05%, Prevalence Ratio (PR)=1.035(95%C.I. 1.034-1.036), attributable fraction in the exposed (AFE)=3.28%(3.18-3.37%), P<10-300) and legalization (4.66+0.09%, PR=1.155(1.153-1.158), AFE=12.91% (12.72-13.10%), P<10-300) were associated with increased SMI vs. illegal status (4.26+0.04%).

Conclusions: Data show all four indices of mental ill-health track cannabis exposure and are robust to multivariable adjustment for ethnicity, socioeconomics and other drug use. MH deteriorated with cannabis legalization. Together with similar international reports and numerous mechanistic studies preventative action to reduce cannabis use-exposure is indicated.

Analysis of ‘Mental Health Patients 2021-2022 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/meetnagadia/district-wise-mental-health-patients-20212022 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Title

District Wise Number of Mental Health Patients in year 2021-2020 in Country India State Karnataka

Description

District Wise number of mental health patients such as severe mental illness, common mental disorder, alcohol, and substance abuse, cases referred to higher centers, suicide attempt cases

Contributor

Karnataka, Health and Family Welfare Department, Karnataka

Sectors

Health and Family welfare › Health

Source

Karnataka data government Click Here to visit the website

Group Name

Department of Health and Family Welfare

--- Original source retains full ownership of the source dataset ---