Work Injuries and Mental Health Problems
Bibliographic record
Abstract
Work injuries and mental health problems continue to inflict hardship on workers while imposing severe costs on organizations. Yet no comprehensive attempt to summarize the relationship between work injuries and mental health problems exists despite evidence of their association. Further, theoretical development of the relationship between work injuries and mental health problems has stalled in recent years. I address these issues by conducting three studies to explain the association between work injuries and mental health problems, conditions shaping and mechanisms underlying this association, and the expected trajectory of mental health problems prior to and following a work injury. Study 1 is the first comprehensive meta-analysis to look at the relationship between work injuries and mental health problems, ordering effects (i.e., work injuriesmental health problems, mental health problemswork injuries), and key conditions. Study 1 indicates the association between work injuries and mental health problems is small but robust, with larger effect sizes emerging when mental health problems are measured following a work injury as opposed to preceding a work injury. Study 2 examines cognitive mechanisms linking work injuries and mental health, as well as a key social condition distinguishing the work injury-mental health problems relationship using data from the Canadian Longitudinal Study of Aging (Raina et al., 2009). Study 2 results suggest maladaptive cognitions link prior work injury with later mental health problems, and memory-related issues link prior mental health problems with later work injury. Further, some evidence emerged for the role of social support mitigating the relationship between prior work injury and later mental health problems, but not vice versa. Finally, Study 3 examines the relationship between work injuries and mental health over time through an intensive longitudinal survey of young workers. Results from this study indicate that young workers tend to be resilient when confronting a work injury, with minor initial differences in mental health. Altogether, the results from these studies have important implications for occupational health and safety initiatives surrounding the prevention, recovery, and reporting of work injuries and mental health problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".