ArticleJournal of occupational health2026
Machine learning in the analysis of mental health at work: a scoping review.
Article in Journal of occupational health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Identification of hidden subtypes in occupational health examinations and their biomedical characteristics using graph-enhanced deep representation learning.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesThis scoping review aimed to assess the role of machine learning in workplace mental health research by systematically analyzing existing studies to understand current methodologies, applications, and trends.
methodsWe conducted a comprehensive search across multiple databases, including EBSCO, Scopus, ProQuest, Web of Science, PsycINFO, IEEE, and ACM, screening a total of 5600 abstracts. Altogether, we analyzed 92 journal articles, conference papers, and book chapters published before September 2025.
resultsSince 2020, there has been a notable increase in publications on the topic. Studies have mainly employed cross-sectional designs (73%) and workplace questionnaires (51%) targeting specific occupational groups (67%), particularly from Asia excluding China (41%). Supervised learning methods, such as Random Forest and Neural Networks, have been frequently utilized to investigate conditions like depression, burnout, and anxiety. Most studies predicting mental health at work using machine learning are currently conducted by data scientists as single-measurement studies, whereas longitudinal studies from medicine, epidemiology, social sciences, or behavioral sciences are comparatively rare. In the context of machine learning, prediction denotes the model's ability to infer outcomes based on input data. However, most publications do not systematically analyze the temporal dynamics of mental health or forecast mental health outcomes from an epidemiological perspective.
conclusionsThe application of machine learning in occupational mental health research remains in its preliminary stages, with a primary focus on methodology and computer science. The review highlights the necessity for interdisciplinary collaboration to fully leverage the potential of machine learning in advancing occupational health research.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.