A description of musculoskeletal injuries in a Canadian police service
Bibliographic record
Abstract
OBJECTIVES: Police officers run a risk of injury that is higher than in most other occupations. This study aims to quantify injury prevalence and identify common musculoskeletal injuries (MSIs) among police officers, using injury data from a municipal police service in Alberta, Canada. MATERIAL AND METHODS: This is a descriptive study based on a secondary data analysis of the MSIs reported to the police service over a 41-month period; January 1, 2013 - June 2, 2016. Data from 1325 active police officers were examined, and injury prevalence was reported according to sex, injury diagnosis, the body part injured, and the work area. RESULTS: The prevalence of strains and sprains was very high, at 89.2%. The back and shoulder were most frequently affected. Overall, injury proportions did not differ significantly across work areas. The injury risk was age-related but no significant differences in injuries between sexes were identified. CONCLUSIONS: Minor injuries such as strains and sprains occur frequently in the police occupation. Future research should focus on specific risk factors for MSIs in police officers in order to aid prevention. Int J Occup Med Environ Health. 2020;33(1):59-66.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".