Assessing psychological distress of healthcare workers with and without work injuries: The role of job control
Bibliographic record
Abstract
INTRODUCTION: The study investigates the relationship between work-related injuries, psychological distress, and the influence of perceived job control on healthcare workers, using Bakker and Demerouti's (2007) job demands-resources model as theoretical grounding. METHOD: We analyzed data from 610 healthcare workers (81.1% female) at a northern UK hospital, incorporating both self-reported and organizationally recorded work injury incidents over the three years preceding the survey, along with measures of psychological distress and perceived job control. RESULTS: Unexpectedly, we found that the occurrence of work-related injuries, irrespective of the method of reporting, is not related to lower psychological distress among those employees who report a high level of job control. This relationship holds even when adjusting for various demographic and occupational variables. CONCLUSIONS AND PRACTICAL APPLICATIONS: Given the prevalence of work injuries in the healthcare sector, our findings suggest a need for a deeper exploration into how job characteristics might interact to offset the consequences of work injuries, challenging existing assumptions and opening new avenues for research into the psychology of workplace safety.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".