Mental health outcomes among urban public transport workers: A systematic literature review
Bibliographic record
Abstract
Although abundant evidence suggests that Urban Public Transport (UPT) workers are at high risk of poor mental health, there is no consensus on the exposures that explain these outcomes, nor on the effectiveness of mental health interventions in this occupational group. This study aims to systematically review the literature on the mental health of UPT workers to assess the effects of occupational exposures and interventions. A systematic review was conducted following the PRISMA guidelines. The literature search was performed from 1990 to December 2021 in three databases (PubMed, Scopus, and the Web of Science). The included observational, experimental, intervention and qualitative studies were critically appraised and assessed for risk of bias. A narrative evidence synthesis was conducted by mental health outcomes, occupational exposures, and intervention categories. The database search yielded 1383 records. A total of 83 studies (49 cross-sectional, 8 longitudinal, 8 experiments, 7 interventions, and 11 qualitative) met the inclusion criteria. Bus drivers were the most studied population, followed by metro and train drivers. The included studies covered eleven mental health outcomes: sleep problems, fatigue and recovery needs, alcohol and substance use, PTSD, panic disorders, depression, anxiety, psychopathology symptoms, psychosomatic symptoms, and psychological stress. Work organization-related stressors (work content, workload and pace, working hours, participation and control, career development, status and salary, role in the organization, and interpersonal relationships) and occupational safety risks are the main predictors of negative mental health outcomes. Most intervention studies focus on the management of PTSD symptoms of UPT workers exposed to safety hazards. The results of this review contribute to the consensus on the antecedents of negative mental health outcomes among UPT workers, as well as to the identification of intervention targets and promising research lines for the development of this study field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".