{"title":"Impact of pandemic-related worries on mental health in India from 2020 to 2022","authors":"Youqi Yang, Anqi Sun, Lauren Zimmermann, Bhramar Mukherjee","doi":"10.1038/s44184-024-00101-x","DOIUrl":"10.1038/s44184-024-00101-x","url":null,"abstract":"This study examines how pandemic-related worries affected mental health in India’s adults from 2020 to 2022. Using data from the Global COVID-19 Trends and Impact Survey (N = 2,576,174), it explores the associations between worry variables (financial stress, food insecurity, and COVID-19-related health worries) and self-reported symptoms of depression and anxiety. Our analysis, based on complete cases (N = 747,996), used survey-weighted models, adjusting for demographics and calendar time. The study finds significant associations between these worries and mental health outcomes, with financial stress being the most significant factor affecting both depression (adjusted odds ratio, aOR: 2.36; 95% confidence interval, CI: [2.27, 2.46]) and anxiety (aOR: 1.91; 95% CI: [1.81, 2.01])). Models with interaction terms revealed gender, residential status, and calendar time as effect modifiers. This study demonstrates that social media platforms like Facebook can effectively gather large-scale survey data to track mental health trends during public health crises.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00101-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ziru Fu, Yu Cheng Hsu, Christian S. Chan, Joyce Liu, Paul S. F. Yip
{"title":"Using hidden Markov modelling to reveal in-session stages in text-based counselling","authors":"Ziru Fu, Yu Cheng Hsu, Christian S. Chan, Joyce Liu, Paul S. F. Yip","doi":"10.1038/s44184-024-00103-9","DOIUrl":"10.1038/s44184-024-00103-9","url":null,"abstract":"Counselling sessions have multiple stages, each with its themes and objectives. This study aimed to apply Hidden Markov Models (HMMs) to analyse counselling sessions from Open Up, an online text-based counselling platform in Hong Kong. The focus was on inferring latent stages over word distributions and identifying distinctive patterns of progression in more versus less satisfying sessions. Transcripts from 2589 sessions were categorized into more satisfying sessions ( $$n=mathrm{1993}$$ ) and less satisfying sessions ( $$n=596$$ ) based on post-session surveys. A message-level HMM identified five distinct stages: Rapport-building, Problem-identification, Problem-exploration, Problem-solving, and Wrap-up. Compared with less satisfying sessions, more satisfying sessions saw significantly more efficient initial rapport building (7.5% of session duration), problem introduction (20.2%), problem exploration (28.5%), elaborated solution development (46.6%), and concise conclusion (8.2%). This study offers insights for improving the efficiency and satisfaction of text-based counselling services through efficient initial engagement, thorough issue exploration, and focused problem-solving.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00103-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Infrastructure development in children’s behavioral health systems of care: essential elements and implementation strategies","authors":"Jeffrey J. Vanderploeg","doi":"10.1038/s44184-024-00102-w","DOIUrl":"10.1038/s44184-024-00102-w","url":null,"abstract":"To improve the outcomes of children’s behavioral health systems, states must invest in expanding infrastructure; however, infrastructure is a commonly used and poorly understood concept. This paper aims to provide a definition of infrastructure in the context of state-level children’s behavioral system of care development and describes five essential infrastructure elements: an integrated governance and decision-making structure; structures and processes for blended and braided funding; a central point of access for information, referral, and linkage; workforce development, training, and coaching in effective practices; and data and quality improvement mechanisms. Suggested implementation activities are offered for each of the five proposed infrastructure components. The important role of public-private partnership, particularly with intermediary organizations, is described, and future directions for research and scholarship are proposed.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00102-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142634085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiyeong Kim, Zhuo Ran Cai, Michael L. Chen, Shawheen J. Rezaei, Sonia Onyeka, Carolyn I. Rodriguez, Tina Hernandez-Boussard, Vladimir Filkov, Rachel A. Whitmer, Eleni Linos, Yong K. Choi
{"title":"Mental health care needs of caregivers of people with Alzheimer’s disease from online forum analysis","authors":"Jiyeong Kim, Zhuo Ran Cai, Michael L. Chen, Shawheen J. Rezaei, Sonia Onyeka, Carolyn I. Rodriguez, Tina Hernandez-Boussard, Vladimir Filkov, Rachel A. Whitmer, Eleni Linos, Yong K. Choi","doi":"10.1038/s44184-024-00100-y","DOIUrl":"10.1038/s44184-024-00100-y","url":null,"abstract":"Informal caregivers of people with Alzheimer’s disease and related dementias (ADRD) are at risk of poor mental health. This study aimed to investigate the feasibility and validity of studying caregivers’ mental stressors using online caregiving forum data (March 2018–February 2022) and natural language processing and machine learning (NLP/ML). NLP/ML topic modeling generated eight prominent topics, which we compared with qualitatively defined themes and the existing caregiving framework to assess validity. Among a total of 60,182 posts, 5848 were mental distress-related; for the ADRD patients (symptoms, medication, relocation, care duty share, diagnosis, conversation strategy) and the caregivers (caregiving burden and support). While we observed novel topics from NLP/ML-defined topics, mostly those were aligned with the existing framework. For feasibility assessment, qualitative title screening was done. The findings shed new light on the potential of NLP/ML text analysis of the online forum for informal caregivers to prepare tailored support for this vulnerable population.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00100-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142634094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yolanda Lau, Amit Bansal, Cassandre Palix, Harriet Demnitz-King, Miranka Wirth, Olga Klimecki, Gael Chetelat, Géraldine Poisnel, Natalie L. Marchant, The Medit-Ageing Research Group
{"title":"Sex differences in the association between repetitive negative thinking and neurofilament light","authors":"Yolanda Lau, Amit Bansal, Cassandre Palix, Harriet Demnitz-King, Miranka Wirth, Olga Klimecki, Gael Chetelat, Géraldine Poisnel, Natalie L. Marchant, The Medit-Ageing Research Group","doi":"10.1038/s44184-024-00093-8","DOIUrl":"10.1038/s44184-024-00093-8","url":null,"abstract":"Emerging evidence suggests that repetitive negative thinking (RNT; i.e., worry and ruminative brooding) is associated with biomarkers of Alzheimer’s disease. Given that women have a greater risk of many neurodegenerative diseases, this study investigated whether worry and brooding are associated with general neurodegeneration and whether associations differ by sex. Exploratory analyses examined whether allostatic load, a marker of chronic stress, mediates any observed relationships. Baseline data from 134 cognitively healthy older adults in the Age-Well clinical trial were utilised. Worry and brooding were assessed using questionnaires. Plasma neurofilament light chain (NfL), a biomarker of neurodegeneration, was quantified using a Meso Scale Discovery assay. We found a positive interaction between brooding and sex on NfL, with higher brooding associated with greater NfL levels in women. No associations were observed between worry/ruminative brooding and allostatic load. These results offer preliminary support that RNT is associated with worse brain health, specifically in women.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00093-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aaron M. McCright, Eric D. Achtyes, Robyn Bluhm, Laura Y. Cabrera
{"title":"Explaining key stakeholders’ preferences for potential policies governing psychiatric electroceutical intervention use","authors":"Aaron M. McCright, Eric D. Achtyes, Robyn Bluhm, Laura Y. Cabrera","doi":"10.1038/s44184-024-00096-5","DOIUrl":"10.1038/s44184-024-00096-5","url":null,"abstract":"In recent years, legislators in many states have proposed laws governing the use of psychiatric electroceutical interventions (PEIs), which use electrical or magnetic stimulation to treat mental disorders. To examine how the PEI views of relevant stakeholder groups (e.g., psychiatrists, patients, caregivers, and general public) relate to preferences for proposed policies governing PEI use, we analyze data from a survey on using one of four PEIs to treat major depressive disorder administered to national samples of the stakeholder groups above. We find that the three non-clinician groups’ similar PEI policy preferences differ significantly from those of psychiatrists—with the greatest divide on policies governing the use of electroconvulsive therapy. This divide between psychiatrists’ and non-clinicians’ PEI policy preferences was greater with access-reducing than with access-expanding policies. We advise policymakers to consider such variation in the preferred availability of PEIs across modalities and stakeholder groups when crafting legislation on these interventions.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00096-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Annika M. Schoene, Suzanne Garverich, Iman Ibrahim, Sia Shah, Benjamin Irving, Clifford C. Dacso
{"title":"Automatically extracting social determinants of health for suicide: a narrative literature review","authors":"Annika M. Schoene, Suzanne Garverich, Iman Ibrahim, Sia Shah, Benjamin Irving, Clifford C. Dacso","doi":"10.1038/s44184-024-00087-6","DOIUrl":"10.1038/s44184-024-00087-6","url":null,"abstract":"Suicide is a complex phenomenon that is often not preceded by a diagnosed mental health condition, therefore making it difficult to study and mitigate. Artificial Intelligence has increasingly been used to better understand Social Determinants of Health factors that influence suicide outcomes. In this review we find that many studies use limited SDoH information and minority groups are often underrepresented, thereby omitting important factors that could influence risk of suicide.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00087-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Advancing community health worker models to support youth and families’ mental health","authors":"Erika L. Gustafson, Stephanie A. Torres","doi":"10.1038/s44184-024-00094-7","DOIUrl":"10.1038/s44184-024-00094-7","url":null,"abstract":"Community health workers (CHWs) have demonstrated effectiveness in delivering EBTs; however, the integration of CHWs in the U.S. mental health system remains limited. This Comment presents key recommendations for optimizing CHW integration into the mental health spectrum of care to better meet the needs of youth. We discuss necessary advancements across domains of practice, research, and policy to support the sustainability of these models.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11522426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Benrimoh, Azeezat Azeez, Jean-Marie Batail, Xiaoqian Xiao, Derrick Buchanan, Igor D. Bandeira, Andrew Geoly, Yaakov Keynan, Ian H. Kratter, Nolan R. Williams
{"title":"Early differences in lassitude predicts outcomes in Stanford Neuromodulation Therapy for difficult to treat depression","authors":"David Benrimoh, Azeezat Azeez, Jean-Marie Batail, Xiaoqian Xiao, Derrick Buchanan, Igor D. Bandeira, Andrew Geoly, Yaakov Keynan, Ian H. Kratter, Nolan R. Williams","doi":"10.1038/s44184-024-00099-2","DOIUrl":"10.1038/s44184-024-00099-2","url":null,"abstract":"Stanford Neuromodulation Therapy (SNT), has recently shown rapid efficacy in difficult to treat (DTT) depression. We conducted an exploratory analysis of individual symptom improvements during treatment, correlated with fMRI, to investigate this rapid improvement in 23 DTT participants from an SNT RCT (12 active, 11 sham). Montgomery–Åsberg Depression Rating Scale item 7 (Lassitude) was the earliest to show improvements between active and sham, as early as treatment day 2. Lassitude score at treatment day 3 was predictive of response at 4 weeks post-treatment and response immediately after treatment. Participants with lower lassitude scores at treatment day 3 had different patterns of sgACC functional connectivity compared to participants with higher scores in both baseline and post-treatment minus baseline analyses. Further work will aim to first replicate these preliminary findings, and then to extend these findings and examine how SNT may affect lassitude and behavioral activation early in treatment.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00099-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"“It happened to be the perfect thing”: experiences of generative AI chatbots for mental health","authors":"Steven Siddals, John Torous, Astrid Coxon","doi":"10.1038/s44184-024-00097-4","DOIUrl":"10.1038/s44184-024-00097-4","url":null,"abstract":"The global mental health crisis underscores the need for accessible, effective interventions. Chatbots based on generative artificial intelligence (AI), like ChatGPT, are emerging as novel solutions, but research on real-life usage is limited. We interviewed nineteen individuals about their experiences using generative AI chatbots for mental health. Participants reported high engagement and positive impacts, including better relationships and healing from trauma and loss. We developed four themes: (1) a sense of ‘emotional sanctuary’, (2) ‘insightful guidance’, particularly about relationships, (3) the ‘joy of connection’, and (4) comparisons between the ‘AI therapist’ and human therapy. Some themes echoed prior research on rule-based chatbots, while others seemed novel to generative AI. Participants emphasised the need for better safety guardrails, human-like memory and the ability to lead the therapeutic process. Generative AI chatbots may offer mental health support that feels meaningful to users, but further research is needed on safety and effectiveness.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-9"},"PeriodicalIF":0.0,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00097-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142514230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}