The relationship between workplace justice and self-evaluated nonfatal occupational accidents among healthcare employees in Taiwan: An observational study
Bibliographic record
Abstract
The relationship between workplace justice and nonfatal occupational accidents in a single-payer healthcare system has rarely been explored. As countries strive to achieve and sustain universal health coverage, healthcare workers' occupational safety and health require greater concerns. We used the data from a national survey conducted on randomly sampled Taiwanese workers. One hundred forty eight males and 567 females, with a total of 715 healthcare workers aged 20 to 65, were analyzed. The workplace scale consisted of 4 subcomponents, including distributive justice, interpersonal justice, information justice, and procedural justice, and was dichotomized into low and high groups in each dimension. Logistic regression models examined the relationship between workplace justice and self-evaluated occupational accidents among healthcare employees. The prevalence of self-evaluated occupational accidents in healthcare employees was 15.54% and 11.64% for men and women, respectively. After adjusting variables such as sociodemographic variables, physical job demands, shift work status, work contract, and psychological job demands, regression analyses indicated that health employees with lower distributive justice, interpersonal justice, information justice, and procedural justice were significantly associated with self-evaluated occupational accidents both in males and females. Expanding the study to include healthcare systems in different countries could enhance the generalizability of the findings. Offering specific recommendations for policymakers and healthcare administrators to improve workplace justice and reduce occupational accidents.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".