Work injuries and mental health challenges: A meta‐analysis of the bidirectional relationship
Bibliographic record
Abstract
Abstract The link between work injuries and mental health challenges significantly impacts individuals, organizations, and society. However, an integrated understanding of their relationship is lacking due to fragmented research across various disciplines. Drawing from uncertainty in illness theory, our comprehensive meta‐analysis (147 samples, N = 1,457,562) clarifies the bidirectional relationship between work injuries and mental health challenges. We estimate the average strength of the association, compare temporal ordering (work injuries preceding mental health challenges, and vice versa), explore underlying mechanisms, and identify potential moderating factors. Results from a random‐effects model reveal a moderate association between work injuries and mental health challenges (k = 147, ρ = .21, 95% CI = .19, .24, 95% CR = −.11, .50). Notably, the relationship is stronger when work injuries precede mental health challenges (k = 40, ρ = .23, 95% CI = .18, .29, 95% CR = −.10, .52) compared to the reverse (k = 18, ρ = .11, 95% CI = .03, .19, 95% CR = −.23, .42). Negative cognitions and perceived job demand underlie the bidirectional relationships between work injuries and mental health challenges. These findings highlight the interconnected nature of work injuries and mental health challenges, illustrating the need for comprehensive rehabilitation approaches that integrate physical and psychological care, and paving the way for future research and interventions.
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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 | 0.023 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".