Purpose: The aim of the study was to investigate sickness absence due to mental disorders in human service occupations.
Methods: Participants (n = 1,466,100) were randomly selected from two consecutive national 9-year cohorts from the Statistics Finland population database; each cohort represented a 33% sample of the Finnish population aged 25-54 years. These data were linked to diagnosis-specific records on receipt of sickness allowance, drawn from a national register maintained by the Social Insurance Institution of Finland, using personal identification numbers.
Results: Sociodemographic-adjusted hazard ratios (HRs) for sickness absence due to mental disorders in all human service occupations combined were 1.76 for men (95% confidence interval [CI], 1.70-1.84) and 1.36 for women (95% CI, 1.34-1.38) compared with men and women in all other occupations, respectively. Of the 15 specific human service occupations, compared with occupations from the same skill/education level without a significant human service component, medical doctors, psychologists, and service clerks were the only occupations with no increased hazard for either sex, and the HRs were highest for male social care workers (HR 3.02; 95% CI, 2.67-3.41).
Conclusions: Most human service occupations had an increased risk of sickness absence due to mental disorders, and the increases in risks were especially high for men.
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http://dx.doi.org/10.1016/j.annepidem.2018.12.006 | DOI Listing |
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFJMIR Hum Factors
January 2025
Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras Kuala Lumpur, Malaysia.
Background: Evaluating digital health service delivery in primary health care requires a validated questionnaire to comprehensively assess users' ability to implement tasks customized to the program's needs.
Objective: This study aimed to develop, test the reliability of, and validate the Tele-Primary Care Oral Health Clinical Information System (TPC-OHCIS) questionnaire for evaluating the implementation of maternal and child digital health information systems.
Methods: A cross-sectional study was conducted in 2 phases.
JMIR Ment Health
January 2025
Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
Background: Mental health concerns have become increasingly prevalent; however, care remains inaccessible to many. While digital mental health interventions offer a promising solution, self-help and even coached apps have not fully addressed the challenge. There is now a growing interest in hybrid, or blended, care approaches that use apps as tools to augment, rather than to entirely guide, care.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Division of Services and Interventions Research, National Institute of Mental Health, Bethesda, MD, United States.
Background: Although substantial progress has been made in establishing evidence-based psychosocial clinical interventions and implementation strategies for mental health, translating research into practice-particularly in more accessible, community settings-has been slow.
Objective: This protocol outlines the renewal of the National Institute of Mental Health-funded University of Washington Advanced Laboratories for Accelerating the Reach and Impact of Treatments for Youth and Adults with Mental Illness Center, which draws from human-centered design (HCD) and implementation science to improve clinical interventions and implementation strategies. The Center's second round of funding (2023-2028) focuses on using the Discover, Design and Build, and Test (DDBT) framework to address 3 priority clinical intervention and implementation strategy mechanisms (ie, usability, engagement, and appropriateness), which we identified as challenges to implementation and scalability during the first iteration of the center.
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