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1,558 results for “Mental Health”
Hochfrequente Mental Health Surveillance
<p>Im Rahmen der Mental Health Surveillance (MHS) am Robert Koch-Institut (RKI) werden für eine Auswahl an Indikatoren der psychischen Gesundheit von Erwachsenen basierend auf Surveydaten Zeitreihen bestehend aus gleitenden Drei-Monats-Schätzern und Glättungskurven berechnet. Dadurch sollen Entwicklungen in der psychischen Gesundheit der erwachsenen Bevölkerung in Deutschland mit möglichst geringem Zeitverzug beobachtet und insbesondere negative Entwicklungen frühzeitig erkannt werden. Diese hochfrequente Surveillance wurde ursprünglich vor dem Hintergrund neuer Informationsbedarfe zur Entwicklung der psychischen Gesundheit der Bevölkerung in der COVID-19-Pandemie entwickelt.</p>
Data from: mPRIME Study - Interaction of Insulin Resistance with Cognition, Lifestyle, and Mental Health
<p>The presented datasets were collected within the <em>m</em>PRIME study, a prospective, observational study of the H2020 project Prevention and Remediation of Insulin Multimorbidity in Europe (PRIME) (grant No. 847879). The study investigates the interaction of insulin resistance with cognition, lifestyle, and mental health by combining traditional methods with ambulatory assessment and sensor-based data collection. Recruitment took place between March 2021 and March 2023 at the University Hospital Frankfurt, Germany.</p> <p>The eligibility criteria for the study were as follows: Age above 18 years, no intake of antidiabetic medication, insulin or glucocorticoids, no existing type 1 diabetes mellitus or gestational diabetes, no diagnoses of bipolar I disorder, schizophrenia, organically caused mental disorders and substance dependence, no severe neurological disorders, no current pregnancy or breastfeeding, no non-correctable visual impairments, no participation in medication-related studies within the last 6 months, no use of weight-reducing medications or a diet within the last 3 months, sufficient proficiency in German to complete questionnaires and neuropsychological tests.</p> <p>All participants in the <em>m</em>PRIME study provided written informed consent. The study protocol and procedures were approved by the local ethics committee.</p> <p><strong>Study Design</strong></p> <p>Individuals completed a baseline assessment and a one-week ambulatory assessment. The baseline assessment included: socio-demographic information, blood samples, anthropometric measures, neuropsychological tests, and several questionnaires. In addition, individuals were introduced to smartphone-based ecological momentary assessment (EMA), food protocols, and the use of sensors (continuous glucose monitor, accelerometer). Food protocols and EMA were conducted on three consecutive days, including two weekdays and one weekend day (Thursday to Saturday or Sunday to Tuesday). Several times a day, individuals were prompted via their smartphone to complete a working memory task and answer questions about stress, affect, and food intake. The continuous glucose monitor and accelerometer were worn continuously for 1 week.</p> <p> </p>
Dataset of The distinct influence of different maternal mental health symptom profiles on infant sleep during the first year postpartum: a cross-sectional survey
<p>The distinct influence of different, but comorbid, maternal mental health difficulties, such as postpartum depression, anxiety, or childbirth-related posttraumatic stress disorder (CB-PTSD) on infant sleep is unknown, although maternal mental health was reported to be associated with infant sleep. This paper first aimed to associations between maternal mental health symptoms and infant sleep. Second, it aimed to exploratory obtain maternal mental health symptom profiles from maternal mental health symptoms. Finally, it aimed to investigate the distinct influence of these maternal mental health symptom profiles on infant sleep, when including mediators (i.e., maternal perception of infant temperament and method to fall asleep) and moderators (maternal educational level and infant age).</p> <p>This dataset contains data on the mental health (i.e., CB-PTSD, depression, anxiety) of 410 mothers with an infant aged between 3 to 12 months old. Information on infant sleep and temperament (negative emotionality) was collected via standardised maternal-report questionnaires (City BiTS, EPDS, HADS, BISQ, and IBQ-R very short form). Sociodemographic data such as maternal age, marital status, educational level, infant age, and week of gestation are reported.</p> <p>This dataset is related to: Sandoz, V.; Lacroix, A.; Stuijfzand, S.; Bickle Graz, M.; Horsch, A. Maternal Mental Health Symptom Profiles and Infant Sleep: A Cross-Sectional Survey. <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>, 1625. https://doi.org/10.3390/diagnostics12071625. </p>
Supporting Material for "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review"
<p>This dataset contains all supporting material for the paper "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review", published in the journal Swiss Psychology Open:</p> <p><em>Mack, M., Scarampi, C., Joly-Burra, E., Zuber, S., de Freitas, C., Teixeira, R. and Kliegel, M. (2025) ‘Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review’, Swiss Psychology Open, 5(1), p. 2. Available at: <a href="https://doi.org/10.5334/spo.81.">https://doi.org/10.5334/spo.81</a>.</em></p> <p>It includes the following documents and files:</p> <p><strong>S1. Protocol:</strong> ADVANCE Protocol for desk reviews</p> <p><strong>S2. Search strategy</strong></p> <p><strong>S3. Guidelines for title and abstract screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S4. Guidelines full-text screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S5. Guidelines data extraction:</strong> ADVANCE Guidelines/codebook data extraction</p> <p><strong>data extraction_desk review_switzerland.xlsx</strong></p> <p>This desk review was conducted as part of the ADVANCE project, which aims to enhance our understanding of mental health promotion and prevention. This desk review evaluates the current state of interventions for mental health and cognitive functioning among older adults in Switzerland focusing on the features of these interventions as well as on Swiss-specific contextual factors that contribute to vulnerability and stigma. This results of the desk review has been submitted for publication to 'LIVES Working Papers' and 'Swiss Psychology Open' . The two versions of the desk review differ slightly. The version for LIVES Working Papers, included the results of the Delphi survey and the resulting intervention scenarios. The version for Swiss Psychology Open, did not include the Delphi survey results and the resulting intervention scenarios, but included a more detailed discussion of the review results.</p>
Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.
<p>Dataset for the manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p>The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)</p>
NDVI Raster maps of Scotland for 2013-2016 used to analyse correlations between greenness, mortality and mental health.
<p>These files were used in the analysis for "Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study" Hyam. Submitted to RIO 2020.</p> <p><strong>Extract on construction of data</strong></p> <p>NDVI data was downloaded from the United States Geological Survey (USGS) Land Satellites Data System (LSDS) Science Research and Development (LSRD) (United States Geological Survey 2018). Which produces Level 2 and Level 3 data products from the Level 1 data of instruments aboard Landsat Satellites. For this study Surface Reflectance data generated by the Landsat Surface Reflectance Code (LaSRC) from the Operational Land Imager (OLI) instrument aboard the Landsat 8 satellite was used (United States Geological Survey 2018). The Surface Reflectance NDVI (sr_ndvi) product and Level-2 Pixel Quality Assessment band (pixel_qa) were downloaded for Landsat scenes 204/21, 205/21, 206/21, 204/20, 205/20, 206/20 WRS-2 (NASA 2018) for the calendar years 2013 to 2016. These scenes cover most of Scotland and include all the major urban areas. A full list of the 333 products is given in supplemental material. Suppl. material 2</p> <p>All of Scotland is over 54° North and so for many satellite images the sun is at too low an angle to give reliable surface reflectance data especially in the winter months. Scotland also has an oceanic climate so the ground is often obscured by cloud or mist. To build a detailed, contiguous NDVI map of the whole country therefore requires combining images taken on many satellite passes especially if points are to be sampled multiple times to overcome measurement errors. The images downloaded from USGS were therefore combined. A cloud free version of each NDVI image was created by setting the pixels that corresponded to cloud, snow or water in the Quality Assurance Assessment band to NA. These cloud free images were then combined into a single, mosaic stack of images to cover all of the study area and then averaged down to a single layer as a tiff image. This was done for two seasonal periods, Winter (October, November, December of 2013, 2014, 2015 and 2016 combined with January, February, March of 2014, 2015, 2016) and summer (April, May, June, July, August, September of 2014, 2015, and 2016). The resulting two images covering most of Scotland for winters and summers between 2013 and 2016 and formed the basis of subsequent analysis.</p> <p>These two files are included here along with a list of the Landsat products used to produce them.</p>
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
<p>Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a ’care in the community’ framework.</p>
MHMisinfo - Video-based Mental Health Misinformation Dataset
<p>MHMisinfo-Gold and MHMisinfo-Large datasets, as described in the paper "Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms" (forthcoming at ICWSM 2025). Videos and comments for each dataset are seperately stored in different .csv files</p> <p><strong>Dataset schema, videos</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>video_id</strong></td> <td>ID of the Video, as assigned by their respective platforms</td> </tr> <tr> <td><strong>video_title</strong></td> <td>The title of the video</td> </tr> <tr> <td><strong>video_description</strong></td> <td>The description of the video, given by the video creators</td> </tr> <tr> <td><strong>audio_transcript</strong></td> <td>Text transcription of the video's audio track, as generated by Whisper speech-to-text model</td> </tr> <tr> <td><strong>video_view_count</strong></td> <td>View count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_like_count</strong></td> <td>Like count of the video, at the time of data collection</td> </tr> <tr> <td><strong>video_comment_count</strong></td> <td>Comment count of the video, at the time of data collection</td> </tr> <tr> <td><strong>label_ioi</strong></td> <td>"Information of Interventions" label of video, annotated by experts. 1 = High-quality information on interventions, -1 = Low-quality information on interventions</td> </tr> <tr> <td><strong>label_ebt</strong></td> <td>"Evidence-based Treatment" label of video, annotated by experts. 1 = Encourages evidence-based treatment, -1 = Discourages evidence-based treatment</td> </tr> <tr> <td><strong>label_aoc</strong></td> <td>"Alignment of Consensus" label of video, annotated by experts, 1 = High Alignment with Consensus, -1 = Low Alignment with Consensus</td> </tr> <tr> <td><strong>label</strong></td> <td>Overall mental health misinformation label of the video. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> <tr> <td><strong>platform</strong></td> <td>Platform of the video</td> </tr> </tbody> </table> <p><strong>Dataset schema, comments</strong></p> <table> <tbody> <tr> <th><strong>Column Name</strong></th> <th><strong>Description</strong></th> </tr> <tr> <td><strong>text</strong></td> <td>The raw text of the comment</td> </tr> <tr> <td> <p><strong>commenter_channel_display_name</strong></p> </td> <td>The display name of the user who posted the comment.</td> </tr> <tr> <td> <p><strong>comment_publish_date</strong></p> </td> <td>The time when the comment was orignally published, .</td> </tr> <tr> <td> <p><strong>video_id</strong></p> </td> <td>ID of the Video associated by the platform, as assigned by their respective platforms</td> </tr> <tr> <td> <p><strong>platform</strong></p> </td> <td>Platform of the video associated with the comment</td> </tr> <tr> <td> <p><strong>label</strong></p> </td> <td>Overall mental health misinformation label of the video associated with the comment. 0 = non-MHMisinfo videos, and -1 = MHMisinfo videos</td> </tr> </tbody> </table> <p> </p>
Qualitative Data on Effects of Early and Prolonged Parent-Child Separation: Understanding Mental Health of Separated-Reunited Chinese American Children
<div> <div> <div> <div>Early and prolonged parent-child separation due to parental migration or immigration may result in attachment disruption that can threaten the long-term mental health and functioning of affected children, and these risks can persist following reunification and through adulthood. Although sending infants back to the home country for rearing is often practiced among <em>Chinese</em> immigrants, especially low-income families, research has been sparse in understanding the long-term impact of early and prolonged parent-child separation and reunification on disparities in mental health and functioning among separated-reunited children and the mechanism through which such relationships may operate.</div> <div>Funded by National Institute on Minority Health and Health Disparities (NIMH), we collected semi-structured interview data from 24 parent-child dyads who have experienced separation. The data included interview scrpits with primary coding to understand the mental health impacts, risk/protective factors, and service needs among separated-reunited <em>Chinese</em> American children.</div> </div> </div> </div> <div> </div>
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
Intimate Partner Violence and Mental Health in Migrant Women Living in Spain
<p>This dataset contains data from a convenience sample in a cross-sectional study of intimate partner violence (IPV) and mental health in migrant women living in the Valencian Community of Spain. IPV was assessed using the Revised Composite Abuse Scale Short Form (CASR-SF), the Revised Scale of Economic Abuse (SEA2) and the Cyber Aggression in Relationship Scale (CARS). Mental health was assessed using the PHQ-9 for symptoms of depression and the GAD-7 for symptoms of anxiety.</p>
Dataset for the paper "Mental Health and Burnout during Medical School: Longitudinal Evolution and Covariates" published in PLOS ONE (2024)
<p><strong>Full reference of the paper:</strong></p> <p>Carrard V, Berney S, Bourquin C, Ranjbar S, Castelao E, Schlegel K, et al. (2024) Mental health and burnout during medical school: Longitudinal evolution and covariates. PLoS ONE 19(4): e0295100. <a href="https://doi.org/10.1371/journal.pone.0295100">https://doi.org/10.1371/journal.pone.0295100</a></p>
Gender differences in preferences for mental health apps in the general popu-lation – a Choice-based Conjoint Analysis from Germany
<p><em><span>Background:</span></em><span> Men and women differ in the mental health issues they typically face. This study aims to describe gender differences in preferences for mental health treatment options and specifically tries to identify participants who prefer AI-based therapy over traditional face-to-face therapy. </span></p> <p><em><span>Method:</span></em><span> A nationally representative sample of 2</span><span>,</span><span>108 participants (53% female) aged 18 to 74 </span><span>years </span><span>completed a </span><span>CBCAs</span><span>. Within the CBCA</span><span>,</span><span> participants evaluated twenty choice sets, each describing three treatment variants in terms of provider, content, costs, and waiting time. </span></p> <p><em><span>Results:</span></em><span> Costs (</span><span>relative importance</span><span> [RI] = 55%) emerged as the most critical factor when choosing between treatment options, followed by provider (RI= 31%), content (RI = 10%), and waiting time (RI = 4%). Small yet statistically significant differences were observed between women and men. Women placed </span><span>greater</span><span> importance on the provider</span><span>,</span><span> while men placed </span><span>greater</span><span> importance on cost and waiting time. Age and previous experience with psychotherapy and with mental health apps were systematically related to individual preferences but did not alter gender effects. Only a minority </span><span>(approximately 8%)</span><span> of participants preferred AI-based treatment to traditional therapy. </span></p> <p><em><span>Conclusions:</span></em><span> Overall, affordable mental health treatments </span><span>performed</span><span> by human therapists are consistently favored by both men and women. AI-driven mental health apps should align with user preferences to address psychologist shortages. However, it is uncertain whether they alone can meet the rising demand, highlighting the need for alternative solutions.</span></p> <p><em><span>Keywords: </span></em><span>gender preferences; discrete choice experiment; mental health treatment; artificial intelligence</span></p>
Mental health, physical health, training load and subjective performance during the COVID-19 pandemic – a Swiss elite athletes' cohort study
<p>Dataset of Swiss elite athletes (n=203) participating in a repeated online survey evaluating mental and physical health factors, as well as training and performance related metrics. After the first survey during the first lockdown between April and May 2020, there were monthly follow-up surveys over a 6-month period.</p>
Family Type, Mental and Physical Health of in-school Female Adolescents in Ado-Ekiti, Nigeria
<p>This dataset is based on a cross-sectional survey conducted in Ado-Ekiti, Nigeria among in-school female adolescents. The general objective of the study was to examine the influence of family type on the physical and mental health of in-school female adolescents. The data were obtained in 2017 from in-school female adolescents aged 10-18 years old. The respondents were randomly selected from four purposively selected secondary schools. Two schools were selected from the government-owned public schools and two from schools owned by individuals or private organizations. Nigeria operates a six-year secondary education; three years at the junior secondary school (JSS 1-3) and three years at the senior secondary (SS 1-3). The population for this study excluded students in SS3 because they had just concluded their terminal examination at the time of this study. Using the formula proposed by (Krejcie & Morgan, 1970) for deriving a small sample when the population is known, a sample size of 383 was derived from a population of 1656 female students in the four schools. With 10% added to adjust for non-response, a total of 421 students were involved in the study. The number of respondents allocated to each school was proportional to the size of the female students' population in each school. In each school, respondents were assigned to classes proportional to the size, and the particular respondents were identified using a random number. Data were collected with a self-administered structured questionnaire which was piloted before the actual survey. The data is saved in Stata format. </p> <p>A scale for measuring self-reported mental well was designed using questions adapted from Ross, Mirowsky, & Goldsteen (1990) and (Langton & Berger, 2011). The scale reliability coefficient was 0.85. The students were asked to state how often they experience 22 conditions such as feeling sad, discouraged, lonely, hopeless, worthless, wishing you were dead, having trouble concentrating, having difficulty sleeping, crying, and worried among others. </p> <p>Physical health was measured using a scale generated with questions adapted from (Langton & Berger, 2011; Ross et al., 1990). The students were asked to state the frequency of experiencing seven conditions in the month before the survey: feeling sick, tired, dizzy, having chest pain, a headache, muscle or joint pain, and stomach ache. The response options were every day, more than two times, two times, once, and never, graded 1-5.</p> <p>Family type was categorised in four ways: 1) two-parent and single parent. Two parents comprised of couples who were legally married and those who were living together in a consensual union whereas single parents comprised never married, widowed, divorced, and separated mother or father. 2) two-parent (monogamous), two-parent (polygynous), and single parent 3) two-parent, single father, and single mother 4) two-parent, never married, widowed, divorced, and separated</p>
Dataset: Tema Neuroscience And Mental Health ETF (MNTL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Re-igniting Windrush Folk stories and songs to improve African-Caribbean mental health disparities in the London Boroughs of Lewisham & Greenwich
<p>This cross-disciplinary intergenerational project created a network of experts from multiple fields, aiming to achieve several key objectives. Using A-C Folk Songs and Art methods, it provided support to hub spaces, including training, remuneration for services and connecting them to therapists. The project also embeded culturally relevant narrative therapy techniques in community settings to support proactive positive mental health, whilst ensuring community organisations had access to affordable meeting spaces. Finally, it created opportunities for intergenerational connection, fostering a more cohesive and supportive community environment.</p> <p>A-C communities are 40% more likely than white-British people to come into contact with mental health services and be detained under the Mental Health Act, reflecting a stark historical pattern of structural racism and its ensuing health inequalities within the mental health system (Vige, 2019). Access to mental healthcare services are limited as a result of institutional, cultural and socio- economic exclusion factors related to BME groups (Memon, et al., 2016). The field of clinical psychology often 'assumes a deficit-based-approach' to the mental health of those minoritised by society (Renkly & Bertolini, 2018). This model is problematic with those from A-C groups because it places emphasis on the individual rather than systems of oppression and ignores the ways cultural traditions and communities create supporting mechanisms for mental health. Our approach offers an alternative model.</p> <p>This project uses an augmented generative co-design framework base on Bird et al (2021) where a narrative enquiry (Pinnegar, & Daynes, 2007) is used to engage participants in conversations on folk songs and how these can be utilised to support mental health of the local A-C community. Through a series of workshops we brought together storytellers, A-C elders and young adults 18-85 to gather traditional stories as well as to create new ones. This supported our understanding of both folk stories and song routes and the lessons learnt within them. We used these stories as analogies to map out the socio-cultural ways in which mental health is discussed in African-Caribbean communities, capturing these conversations via film which, after each session, is edited and re-shared in the next session to create a focus for future conversations. </p> <p>During the project we have co-produced a toolkit including: film, workshop plans and thematic analysis of findings. Our work will feed into at least 2 publications which connect the 10 NHS 75 projects (article and policy document) and we plan on publishing at least 2 further works; 1 on methodological insight and the other on research findings.</p> <p>Our work has been shared at the International symposium (1st July 2024 University of Greenwich) linked to mental Health and the Climate crisis, opening possible future avenues of work with international organisations as well as with the Caribbean Association. </p> <p> </p>
Using the Socialise app to collect smartphone sensor data for mental health research: A feasibility study
<p>To investigate the feasibility of collecting smartphone sensor data for mental health research, we tested the Socialise app that was developed at the Black Dog Institute in a group of people with a lived experience of mental health challenges (n=32). Bluetooth, GPS and battery status data were collected at regular intervals (3, 4, 5 or 8 minutes) for 4 weeks. In addition, survey data was collected using the app to investigate the views of participants on user experience and the acceptability of passive data collection for mental health research. No mental health data was collected as part of the feasibility study.</p>
Data Sharing Practices in the MRC Circadian Mental Health Network.
<p>This dataset supports the research conducted within the MRC Circadian Mental Health Network which assesses data sharing practices among Principal Investigators' publications in 2023. This work aims to identify trends, challenges, and inform future recommendations and policies based on the findings. The dataset includes various files that detail the methodology, data collected, and analyses performed.</p> <p> </p> <p><strong>Repository Contents:</strong></p> <ol> <li> <p><strong>Methods and Analysis Report - Data Sharing Practices in the MRC CMHN.pdf</strong></p> <ul> <li>This report provides the methodologies used for selecting and assessing research papers within the network, along with detailed results, tables, and discussions from the evaluation.</li> </ul> </li> <li> <p><strong>CMHN_All_Data.xlsx</strong></p> <ul> <li>An Excel workbook containing: <ul> <li><strong>Sheet 1</strong>: All data and variables collected and analysed for this project.</li> <li><strong>Sheet 2</strong>: A README file that explains each variable and its values.<br><br></li> </ul> </li> </ul> </li> <li> <p><strong>CMHN DataType Scoring.xlsx</strong></p> <ul> <li>An Excel workbook detailing: <ul> <li><strong>Sheet 1</strong>: All datatypes, both code and datasets, evaluated in this study.</li> <li><strong>Sheet 2</strong>: A README explaining the variables evaluated and their specific values.<br><br></li> </ul> </li> </ul> </li> <li> <p><strong>CMHN Data Extraction Survey.pdf</strong></p> <ul> <li>A copy of the Microsoft Form used to systematically evaluate data-sharing practices from selected publications, describing the structured data extraction process used.<br><br></li> </ul> </li> <li> <p><strong>CMHN DataType Scoring Survey.pdf</strong></p> <ul> <li>A Microsoft Form used to assess the types of data (code and datasets) shared.<br><br></li> </ul> </li> <li> <p><strong>Data_CSV_Code.csv</strong></p> <ul> <li>This file is the original, uncleaned dataset directly extracted from the initial response data of the Microsoft Form used in the project. It served as the primary dataset for all subsequent data analysis and code execution within the study.<br><br></li> </ul> </li> <li> <p><strong>CMHN Code.Rmd</strong></p> <ul> <li>An R Markdown file containing the code used for data analysis; predominantly descriptive statistics due to the limited number of papers with shared data.</li> </ul> </li> </ol> <p><strong><br>Recommended Use:</strong> For comparative purposes or further analysis, researchers are encouraged to utilise the cleaned datasets available in "CMHN_All_Data.xlsx" and "CMHN DataType Scoring.xlsx."<br><br><strong>Contact:</strong> For further inquiries, please email us at <a href="mailto:bio_rdm@ed.ac.uk" target="_blank" rel="noopener">bio_rdm@ed.ac.uk</a>.</p>
Twitter mental health classification
<p>A dataset of songs shared by twitter users who have self identified as having an mental health disorder. Also some data in the same format by the control population.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.