Depression affects over 280 million people globally, yet many cases remain undiagnosed or untreated due to stigma and lack of awareness. Social media platforms like X (formerly Twitter) offer a way to monitor and analyze depression markers. This study analyzes Twitter data 90 days before and 90 days after a self-disclosed clinical diagnosis. We gathered 246,637 tweets from 229 diagnosed users. CorEx topic modeling identified seven themes: causes, physical symptoms, mental symptoms, swear words, treatment, coping/support mechanisms, and lifestyle, and conditional logistic regression assessed the odds of these themes occurring post-diagnosis. A control group of healthy users (284,772 tweets) was used to develop and evaluate machine learning classifiers—support vector machines, naive Bayes, and logistic regression—to distinguish between depressed and non-depressed users. Logistic regression and SVM performed best. These findings show the potential of Twitter data for tracking depression and changes in symptoms, coping mechanisms, and treatment use.
{"title":"Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study","authors":"Ranganathan Chandrasekaran, Suhas Kotaki, Abhilash Hosaagrahaara Nagaraja","doi":"10.1038/s44184-024-00107-5","DOIUrl":"10.1038/s44184-024-00107-5","url":null,"abstract":"Depression affects over 280 million people globally, yet many cases remain undiagnosed or untreated due to stigma and lack of awareness. Social media platforms like X (formerly Twitter) offer a way to monitor and analyze depression markers. This study analyzes Twitter data 90 days before and 90 days after a self-disclosed clinical diagnosis. We gathered 246,637 tweets from 229 diagnosed users. CorEx topic modeling identified seven themes: causes, physical symptoms, mental symptoms, swear words, treatment, coping/support mechanisms, and lifestyle, and conditional logistic regression assessed the odds of these themes occurring post-diagnosis. A control group of healthy users (284,772 tweets) was used to develop and evaluate machine learning classifiers—support vector machines, naive Bayes, and logistic regression—to distinguish between depressed and non-depressed users. Logistic regression and SVM performed best. These findings show the potential of Twitter data for tracking depression and changes in symptoms, coping mechanisms, and treatment use.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00107-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790458","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}
Pub Date : 2024-12-06DOI: 10.1038/s44184-024-00108-4
Yijun Chen, Xiaochu Zhang, Rei Akaishi
Smartphones have become an integral part of modern life, raising concerns about their impact on mental health, especially among young people. However, previous studies yielded inconsistent results, possibly due to neglecting the possibility of interactions between offline and online communications. To explore potential interactions among different communication modes (online vs. offline) and communication types (private vs. public), we adopted the experience sampling method to track 418 Japanese individuals over 21 days and analyzed the data using multilevel models and psychometric network models. The findings revealed that digital use has only small direct effects on happiness and loneliness, especially through public (one-to-many) online communication. The increased digital use reduced offline communication time, indirectly influencing loneliness to a large degree. Overall, this study highlights the indirect effects of decreased face-to-face communication and the significant role of one-to-many online communication, which may explain a part of the diverse findings on this issue.
{"title":"Exploring digital use, happiness, and loneliness in Japan with the experience sampling method","authors":"Yijun Chen, Xiaochu Zhang, Rei Akaishi","doi":"10.1038/s44184-024-00108-4","DOIUrl":"10.1038/s44184-024-00108-4","url":null,"abstract":"Smartphones have become an integral part of modern life, raising concerns about their impact on mental health, especially among young people. However, previous studies yielded inconsistent results, possibly due to neglecting the possibility of interactions between offline and online communications. To explore potential interactions among different communication modes (online vs. offline) and communication types (private vs. public), we adopted the experience sampling method to track 418 Japanese individuals over 21 days and analyzed the data using multilevel models and psychometric network models. The findings revealed that digital use has only small direct effects on happiness and loneliness, especially through public (one-to-many) online communication. The increased digital use reduced offline communication time, indirectly influencing loneliness to a large degree. Overall, this study highlights the indirect effects of decreased face-to-face communication and the significant role of one-to-many online communication, which may explain a part of the diverse findings on this issue.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00108-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790481","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}
Pub Date : 2024-12-05DOI: 10.1038/s44184-024-00105-7
Nicholas J. Dobbins, Jacqueline Chipkin, Tim Byrne, Omar Ghabra, Julia Siar, Mitchell Sauder, R. Michael Huijon, Taylor M. Black
Violence, verbal abuse, threats, and sexual harassment of healthcare providers by patients is a major challenge for healthcare organizations around the world, contributing to staff turnover, distress, absenteeism, reduced job satisfaction, and worsening mental and physical health. To enable interventions prior to possible violent episodes, we trained two deep learning models to predict violence against healthcare workers 3 days prior to violent events for case and control patients. The first model is a document classification model using clinical notes, and the second is a baseline regression model using largely structured data. Our document classification model achieved an F1 score of 0.75 while our model using structured data achieved an F1 of 0.72, both exceeding the predictive performance of a psychiatry team who reviewed the same documents (0.5 F1). To aid in the explainability and understanding of risk factors for violent events, we additionally trained a named entity recognition classifier on annotations of the same corpus, which achieved an overall F1 of 0.7. This study demonstrates the first deep learning model capable of predicting violent events within healthcare settings using clinical notes, surpassing the first published baseline of human experts. We anticipate our methods can be generalized and extended to enable intervention at other hospital systems.
{"title":"Deep learning models can predict violence and threats against healthcare providers using clinical notes","authors":"Nicholas J. Dobbins, Jacqueline Chipkin, Tim Byrne, Omar Ghabra, Julia Siar, Mitchell Sauder, R. Michael Huijon, Taylor M. Black","doi":"10.1038/s44184-024-00105-7","DOIUrl":"10.1038/s44184-024-00105-7","url":null,"abstract":"Violence, verbal abuse, threats, and sexual harassment of healthcare providers by patients is a major challenge for healthcare organizations around the world, contributing to staff turnover, distress, absenteeism, reduced job satisfaction, and worsening mental and physical health. To enable interventions prior to possible violent episodes, we trained two deep learning models to predict violence against healthcare workers 3 days prior to violent events for case and control patients. The first model is a document classification model using clinical notes, and the second is a baseline regression model using largely structured data. Our document classification model achieved an F1 score of 0.75 while our model using structured data achieved an F1 of 0.72, both exceeding the predictive performance of a psychiatry team who reviewed the same documents (0.5 F1). To aid in the explainability and understanding of risk factors for violent events, we additionally trained a named entity recognition classifier on annotations of the same corpus, which achieved an overall F1 of 0.7. This study demonstrates the first deep learning model capable of predicting violent events within healthcare settings using clinical notes, surpassing the first published baseline of human experts. We anticipate our methods can be generalized and extended to enable intervention at other hospital systems.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00105-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788036","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}
Accumulating research on mental health emphasizes the general factor of psychopathology (p-factor) that unites various mental health issues. This study develops a psychopathological vulnerability assessment for youths, evaluating its psychometric properties and clinical utility. An umbrella review conceptualized multifactor psychopathological vulnerability, leading to a 57-item pool. A total of 11,224 individuals participated in this study. The resulting 22-item psychopathological vulnerability index (PVI) fitted the unidimensional Rasch model, demonstrating a person separation reliability of 0.78 and a Cronbach’s alpha of 0.84. Cut-off points of 11 and 5, derived from latent class analysis, were used to distinguish vulnerable and high-protection populations. The PVI’s concurrent and predictive hit rates ranged from 36.00% to 53.57% in clinical samples. The PVI concretized the vulnerability–stress model for identifying at-risk youths and may facilitate universal interventions by integrating the theoretical foundations of bifactor S-1 models with key symptoms from network models for theoretically grounded approaches.
{"title":"Development of the psychopathological vulnerability index for screening at-risk youths: a Rasch model approach","authors":"Yujing Liao, Haitao Shen, Wenjie Duan, Shanshan Cui, Chunxiu Zheng, Rong Liu, Yawen Jia","doi":"10.1038/s44184-024-00106-6","DOIUrl":"10.1038/s44184-024-00106-6","url":null,"abstract":"Accumulating research on mental health emphasizes the general factor of psychopathology (p-factor) that unites various mental health issues. This study develops a psychopathological vulnerability assessment for youths, evaluating its psychometric properties and clinical utility. An umbrella review conceptualized multifactor psychopathological vulnerability, leading to a 57-item pool. A total of 11,224 individuals participated in this study. The resulting 22-item psychopathological vulnerability index (PVI) fitted the unidimensional Rasch model, demonstrating a person separation reliability of 0.78 and a Cronbach’s alpha of 0.84. Cut-off points of 11 and 5, derived from latent class analysis, were used to distinguish vulnerable and high-protection populations. The PVI’s concurrent and predictive hit rates ranged from 36.00% to 53.57% in clinical samples. The PVI concretized the vulnerability–stress model for identifying at-risk youths and may facilitate universal interventions by integrating the theoretical foundations of bifactor S-1 models with key symptoms from network models for theoretically grounded approaches.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775310","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}
Pub Date : 2024-11-30DOI: 10.1038/s44184-024-00098-3
Yu Jin, Yinjie Fan, Jian He, Amanda Wilson, Yi Li, Jiaqi Li, Yajun Bu, Yuanyuan Wang
Previous studies have explored the associations between parental and offspring’s depression and parent-child communication. However, few studies have investigated their symptomatic associations and potential sex differences. Therefore, this study aims to examine their associations and sex differences in parents and offspring. Based on the China Family Panel Studies (CFPS)-2020 study, depressive symptoms and parent-child communication were measured by the 8-item Center for Epidemiologic Studies Depression Scale (CESD-8) and independent questions, respectively. Network analysis was used to investigate the associations and to compare the sex differences of parents and offspring. A total of 1710 adolescents were included after cleaning process (N = 28,530). There were significantly stronger associations in boys’ “anhedonia” and “arguments with parents”, and in girls’ “happiness” and parents’ “joyfulness”. Furthermore, there were same-sex depression associations between children and parents (e.g., boys’ “despair”–fathers’ “joyfulness”; girls’ “anhedonia”–mothers’ “loneliness”). These results would help us to better understand the in depression and communication nuanced associations and to develop effective strategies for improving parental and offspring’s mental health.
{"title":"Symptomatic associations and sexual differences in depression and communication","authors":"Yu Jin, Yinjie Fan, Jian He, Amanda Wilson, Yi Li, Jiaqi Li, Yajun Bu, Yuanyuan Wang","doi":"10.1038/s44184-024-00098-3","DOIUrl":"10.1038/s44184-024-00098-3","url":null,"abstract":"Previous studies have explored the associations between parental and offspring’s depression and parent-child communication. However, few studies have investigated their symptomatic associations and potential sex differences. Therefore, this study aims to examine their associations and sex differences in parents and offspring. Based on the China Family Panel Studies (CFPS)-2020 study, depressive symptoms and parent-child communication were measured by the 8-item Center for Epidemiologic Studies Depression Scale (CESD-8) and independent questions, respectively. Network analysis was used to investigate the associations and to compare the sex differences of parents and offspring. A total of 1710 adolescents were included after cleaning process (N = 28,530). There were significantly stronger associations in boys’ “anhedonia” and “arguments with parents”, and in girls’ “happiness” and parents’ “joyfulness”. Furthermore, there were same-sex depression associations between children and parents (e.g., boys’ “despair”–fathers’ “joyfulness”; girls’ “anhedonia”–mothers’ “loneliness”). These results would help us to better understand the in depression and communication nuanced associations and to develop effective strategies for improving parental and offspring’s mental health.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00098-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758096","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}
Pub Date : 2024-11-27DOI: 10.1038/s44184-024-00104-8
Judith Cukor, Zhenxing Xu, Veer Vekaria, Fei Wang, Mark Olfson, Samprit Banerjee, Gregory Simon, George Alexopoulos, Jyotishman Pathak
Despite the high correlation between anxiety and depression, little remains known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling. Three depression subgroups showed symptom improvement, while three showed nonresponses. Similar patterns were observed in the six anxiety subgroups. Multinomial regression was used to associate patient characteristics with trajectory subgroup membership. Compared to patients in the remission group, factors associated with depressive symptom nonresponse included older age and lower depression severity.
{"title":"Longitudinal trajectories of symptom change during antidepressant treatment among managed care patients with depression and anxiety","authors":"Judith Cukor, Zhenxing Xu, Veer Vekaria, Fei Wang, Mark Olfson, Samprit Banerjee, Gregory Simon, George Alexopoulos, Jyotishman Pathak","doi":"10.1038/s44184-024-00104-8","DOIUrl":"10.1038/s44184-024-00104-8","url":null,"abstract":"Despite the high correlation between anxiety and depression, little remains known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling. Three depression subgroups showed symptom improvement, while three showed nonresponses. Similar patterns were observed in the six anxiety subgroups. Multinomial regression was used to associate patient characteristics with trajectory subgroup membership. Compared to patients in the remission group, factors associated with depressive symptom nonresponse included older age and lower depression severity.","PeriodicalId":74321,"journal":{"name":"Npj mental health research","volume":" ","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44184-024-00104-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142737666","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}
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.
{"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}
Pub Date : 2024-11-21DOI: 10.1038/s44184-024-00103-9
Ziru Fu, Yu Cheng Hsu, Christian S. Chan, Joyce Liu, Paul S. F. Yip
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.
咨询过程分为多个阶段，每个阶段都有自己的主题和目标。本研究旨在应用隐马尔可夫模型（HMMs）分析香港在线文本咨询平台 Open Up 的咨询过程。研究的重点是推断单词分布的潜在阶段，并识别满意度较高与较低的会话中的独特进展模式。根据会后调查，来自 2589 个会话的文字记录被分为满意度较高的会话（n = 1993）和满意度较低的会话（n = 596）。信息级 HMM 确定了五个不同的阶段：建立关系、发现问题、探索问题、解决问题和总结。与满意度较低的会话相比，满意度较高的会话在初步建立友好关系（占会话时间的 7.5%）、问题介绍（20.2%）、问题探索（28.5%）、详细制定解决方案（46.6%）和简明总结（8.2%）方面的效率明显更高。这项研究为通过高效的初步接触、彻底的问题探索和集中的问题解决来提高基于文本的咨询服务的效率和满意度提供了启示。
{"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}
Pub Date : 2024-11-14DOI: 10.1038/s44184-024-00102-w
Jeffrey J. Vanderploeg
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.
{"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}
Pub Date : 2024-11-14DOI: 10.1038/s44184-024-00100-y
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
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.
{"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}
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