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1,558 results for “Mental Health”
Mental Health in Social Networks with Machine Learning Algorihtms
<p>The <strong>DatasetMH.xlsx</strong> excel corresponds to a corpus of mental health in social networks labelled with polarity and stigma. In particular, the corpus consists of 2,287 comments labelled with polarity (positive, negative, neutral) and stigma from comments on Instagram posts about celebrity mental health disclosures:</p> <ol> <li>Polarity: It consists of giving a positive, negative or neutral/undefined value to the comments in response to the disclosure or description of the symptomatology in the post. Positive polarity reflects understanding, encouragement or even admiration of the publication. E.g., “Cheer up, we love you".”. Negative polarity is assigned when the person expresses negative opinions, usually questioning the post with ironic, sarcastic or even mocking and disparaging comments. E.g., “how you show that you don't know what depression or anxiety is, shame on you!”. Neutral or undefined polarity is assigned in cases where no clear opinion is detected or can be interpreted in both directions. E.g., “take medication, it will help you" "and your partner?”</li> <li>Stigma: stigmatising responses to comments are behaviours in which negative beliefs and emotions towards MH problems are expressed. Stigma manifests in a variety of forms including rejection and anger against the person, which may extend to contempt or mockery, belittling their problem. E.g.,"What a desire to draw attention to yourself"; "what you have is a story"; "you're so inconsistent and seeking the limelight". Because socially we know that "stigma is wrong" many rejection comments are made in an ironic or sarcastic way. E.g., and how do you write on insta?"; "better information from someone who doesn't have a current account". Additionally, anger is shown by arguing that such posts "trivialise or commercialise" MH. E.g.,"don't come and tell me your false stories of overcoming, without even knowing what it is to work...". Other times the stigma manifests itself as pity or sorrow for the person. E.g.,“It breaks my heart”; “poor thing”.</li> </ol> <p>The file <strong>DatasetMH_Emotions.xlsx</strong> corresponds to a corpus of mental health in social networks labelled with emotions. In particular, the corpus consists of 2,287 comments labelled with five emotions plus a neutral class from comments on Instagram posts about celebrity mental health disclosures. These emotions are:</p> <ul> <li>Love/admiration: This emotion involves messages where admiration, approval and love are closely related.</li> <li>Gratitude: the messages imply a sincere appreciation for sharing mental health-related content on social networks.</li> <li>Comprehension/empathy/identification: The messages involve interest in and understanding of the message, including self-identification with the situation or context.</li> <li>Sadness: This primary emotion is produced by events that are not pleasant and that denote heaviness. It includes many manifestations of pity for the person.</li> <li>Anger/contempt/mockery: This emotion involves responses of irritation and attacks on the person as ridiculous and superficial.</li> <li>Neutral: This category corresponds to messages without emotions.</li> </ul> <p>The labelling process of both datasets was divided into two phases: an initial phase with a pilot corpus (N = 787 comments) and a second phase focused on the development of the corpus with all the comments of the selected posts (N = 21151). The same methodology was followed in both phases: once the comments were collected, the corpus was cleaned, and then two independent experts were responsible for labelling each category. A third expert then reviewed the comments to resolve discrepancies. In the third and final phase, a final corpus for application to the machine learning algorithms is built from the large corpus (N = 2287).</p> <p>Classification models are a set of machine learning algorithms developed to assess emotional response, i.e. polarity, stigma and emotions in social networks, based on previously developed datasets (<strong>DatasetMH_Emotions.xlsx</strong>, <strong>DatasetMH.xlsx</strong>).</p>
Survey data on the demographics, motivations, mental-health issues and regrets of r/RoastMe posters
<p>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.</p>
Suicide, mental health & economic statistics
<p>The dataset contains data on world suicide rate (per 100 000 people, 2000, 2005, 20010, 2016 years), mental disorders rate (population%, 2017), share of population with substance use disorders (population%, 1990-2016), GDP per capita by PPP (current US$, 1990-2017), adjusted net national income per capita (current US$, 2016) and unemployment rate (population %, 1991-2017), which are then analysed and compared by country. </p>
Accuracy of online survey assessment of mental disorders and suicidal thoughts and behaviors in Spanish university students. Results of the WHO World Mental Health-International College Student initiative.
<p>This dataset contains clinical data about 287 university students that participated in a clinical reappraisal study with the objective of examining the accuracy of WMH-ICS online screening scales for evaluating four common mental disorders (Major Depressive Episode, Mania/Hypomania, Panic Disorder, Generalized Anxiety Disorder) and suicidal thoughts and behaviors used in a survey of Spanish university students(UNIVERSAL project).</p>
Telemedicine's Impact on Mental Health
<p>This study evaluates the impact of telemedicine on mental health outcomes during the COVID-19 pandemic by analyzing data from various mental health service providers. The findings indicate significant improvements in access to care, treatment adherence, and overall mental health outcomes, highlighting telemedicine's efficacy in delivering mental health services during the pandemic.</p>
Enhancing Collaborative Care in Schizophrenia: A Comparative Analysis of ICF Core Sets Assessments by Occupational Therapy and Mental Health Social Work Students
<p>This repository contains the R code, dataset, and README file for the network analysis of ICF (International Classification of Functioning, Disability, and Health) assessments. The study compares the assessments conducted by occupational therapy students and mental health social work students, analyzing centrality measures and Bridge Expected Influence within their respective networks.</p>
Study data for the journal article "Mental health of individuals at increased suicide risk after hospital discharge and initial findings on the usefulness of a suicide prevention project in Central Switzerland"
<p>Anonymized raw data of our cross-sectional survey study.</p> <p>id = study participant ID; se1-se10 = questions of the General Self-Efficacy Scale; sm1-sm5 = questions of the Self-Management Self-Test; hl1-hl12 = questions of the Health Literacy Questionnaire (Swiss version); prisms = question on the utilization of the PRISM-S technique; sp1-sp4 = questions on the utilization and perceived usefulness of the personal safety plan; app1-app7 = questions on the utilization and perceived usefulness of the SERO app; ensa = question on the participation in ensa courses; sex-income = sociodemographic questions</p>
Data set of study Examining the impact of cognitive control and discrimination on mental health outcomes in diverse Pakistani and Afghan communities
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>This dataset is part of a study examining the impact of cognitive control and discrimination on mental health outcomes among diverse Pakistani and Afghan communities. It includes demographic variables such as age, gender, and ethnicity, as well as psychological measures assessing cognitive control, discrimination experiences, and various mental health outcomes.</p> </div> </div> </div> </div> <div> <div> <div> <div> </div> <div><span>4o</span></div> <span></span></div> </div> </div> <div> </div> <div> <div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> </div> <div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> </div> </div> </div> </div> </div> <div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Mental health network of Gipuzkoa dataset (2015) for benchmark analysis
<p>This dataset contains data from the Mental Health System of Gipuzkoa, which are described in the manuscript: "Garcia-Alonso, CR., Almeda, N., Salinas-Pérez, JA., Gutiérrez-Colosía, MR., Iruin-Sanz, A., & Salvador-Carulla., L. (2021). Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care: The case of Gipuzkoa (Basque Country, Spain)". This manuscript has been submitted to Plos One journal.</p> <p>This research focused on developing an analytical process for assessing the performance of the Mental health (MH) system of Gipuzkoa and identifying benchmark and target-for-improvement catchment areas. For doing so a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence was used. The units of analysis, which are considered the decision-making units, were the 13 catchment areas defined by a reference MH centre. The following indicators were assessed: relative technical efficiency, stability and entropy to guide organizational interventions.</p> <p>Main results of the analyses pointed out that the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). With regards to performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services.</p>
Data set - Stress, Mental Health and Sociocultural Adjustment in Third Culture Kids: The Mediating Roles of Resilience and Family Functioning
<p>this data set contains data derived from a cross-sectional study which explores the contributions of proximal and contextual factors in the adjustment process of a sample of internationally mobile children and adolescents having relocated to Switzerland. </p> <p>scales include child perceived stress (PSS-C; White, 2014), acculturative stress (ASIC; Suarez-Morales et al., 2007), resilience (CYRM-12; Liebenberg et al., 2013), mental health difficulties SDQ (R. Goodman, 1997), socio cultural adjustmen (SCAS-Child; Ward & Kennedy, 1999) and family functioning (McMaster Family Assessment Device (Epstein et al., 1983)). </p> <p>child age, arrival in Switzerland and cemographic information on country of origin are included</p>
The Influence of Land Surface Temperature on Mental Health in Lausanne - Data
<p>Dataset contains LST data for Lausanne used for the report.</p> <p>GeoPackage is in ESPG:21781 and provides coordinated in ESPG:4326 as well.</p> <p>Data contains mean and median LST for Summer (June - August) 1998, 2008 and 2018 and the delta. The delta is calculated by subtractig the data from 2018 (for example deltalstmedian0818 = lst_2018 - lst_2008).</p> <p>Find the code here: https://github.com/aamir-s18/InfluenceLSTGAF</p>
Mental Health
<p>Ejercicio para la práctica de Workflow de Ciclo de Vida de los Datos (UC-UIMP). Se ha utilizado una base de datos encontrada en Kaggle.No dispone de DOI. Se ha borrado el repositorio en Kaggle :(</p>
Institutional views on researcher mental health
<p>This dataset contains qualitative data about institution's views on researcher mental health. </p> <p>The data was collected during the PRIDE 2023 Masterclass in Dubrovnik on the 6 September 2023, during a 1,5 hours long workshop about Researcher Mental Health. Participants represented professionals of doctoral education from 15 European universities or research performing organisations.</p> <p>Participants were asked about three main issues:</p> <p>1, How does the mental wellbeing of researchers affect your organisation, the work environment, and research culture? How do you know that there is a mental health/wellbeing problem? (<a href="https://zenodo.org/api/files/6204babb-c7de-4928-adae-c5e4827982df/padlet-Institutional%20awareness.pdf">padlet-Institutional awareness.pdf</a>)</p> <p>2, What are the key elements of a healthy academic workplace at your institutions? What is in place and what is still missing? (<a href="https://zenodo.org/api/files/6204babb-c7de-4928-adae-c5e4827982df/padlet-Institutional%20requirements.pdf">padlet-Institutional requirements.pdf</a>)</p> <p>3, What actions are necessary for a healthy working environment? What interventions do you think are needed at your organisation? (<a href="https://zenodo.org/api/files/6204babb-c7de-4928-adae-c5e4827982df/padlet-institutional%20actions.pdf">padlet-institutional actions.pdf</a>)</p> <p>Participants used Padlet to answer these questions and reflect on them during the workshop. The responses are anonymous. </p> <p>Altogether 27 participants contributed to this dataset. </p> <p>The presentation slides used during the workshop are available here: <a href="https://doi.org/10.5281/zenodo.8327451">https://doi.org/10.5281/zenodo.8327451</a></p> <p> </p>
Evaluating Risk Progression in Mental Health Chatbots with Escalating Prompts Dataset
<p>Excel dataset for "Evaluating Risk Progression in Mental Health Chatbots with Escalating Prompts" manuscript. </p>
Cardioprotective and Mental Health Benefits of the MIND Diet Combined With Forest Bathing
ClinicalTrials.gov study NCT06222632. IPD Sharing: YES. Countries: 1. Publications: 18.
Improving Student Mental Health: Adaptive School-based Implementation of CBT
ClinicalTrials.gov study NCT03541317. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Feasibility and Acceptability of W-GenZD vs CBT-light Teletherapy for Adolescents Seeking Mental Health Services
ClinicalTrials.gov study NCT05372913. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Text 2 Connect- Texting Intervention for Mental Health Treatment Utilization
ClinicalTrials.gov study NCT04560075. IPD Sharing: YES. Countries: 1. Publications: 1.
Collaborative Perinatal Mental Health and Parenting Support in Primary Care
ClinicalTrials.gov study NCT02724774. IPD Sharing: NO. Countries: 1. Publications: 3.
Musically-Guided Paced Breathing Improves Mental Health in War-Affected Adolescents
ClinicalTrials.gov study NCT06988800. IPD Sharing: NO. Countries: 1. Publications: 1.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.