Occupational injury risk among ambulance officers and paramedics compared with other healthcare workers in Victoria, Australia: analysis of workers’ compensation claims from 2003 to 2012
Bibliographic record
Abstract
OBJECTIVE: To investigate occupational risk of musculoskeletal (MSK) and mental injury among ambulance officers and paramedics, and compare with nurse professionals, social and welfare professionals, and carers and aides in Victoria, Australia, using workers' compensation (WC) claims statistics. METHODS: Data were retrieved from the Victorian Compensation Research Database (CRD). Analysis was restricted to claims received between 1 July 2003 and 30 June 2012. WC claim rates were calculated using labour force statistics, and expressed per 1000 full-time equivalent workers. Adjusted HRs with 95% CIs for injury risk were estimated using multivariable regression modelling. RESULTS: Ambulance officers and paramedics had an upward trend in WC claim rates for all injuries and the highest rates for MSK and mental injury, in comparison with other healthcare workers during the study period. In the 2009-2012 time period, ambulance officers and paramedics' risk of lower back MSK and mental injury was approximately 13 times higher than nurse professionals, HRs 57.6 vs 4.4 and 17.77 vs 1.29, respectively. Social and welfare professionals had the second highest risk of mental injury, which was up to threefold greater than in nurses. Carers and aides and nurse professionals had similar HRs overall for all injury categories. CONCLUSIONS: Differential patterns of MSK and mental injury exist among healthcare occupational groups in Victoria, Australia. Given the significant findings, especially the high risks among ambulance personnel, future research should focus on the circumstances of injury to improve understanding and inform prevention programmes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".