Prevalence of musculoskeletal disorders among Canadian firefighters: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Firefighters are set to respond to a number of dynamic demands within their roles that extend well beyond fire suppression. These tasks (i.e., heavy lifting, awkward postures) and their unpredictable nature are likely contributing factors to musculoskeletal disorders (MSDs). Several individual studies have assessed the prevalence of MSDs among Canadian firefighters. Therefore, a systematic review and meta-analysis was conducted to critically appraise the quality of the body of available literature and to provide pooled point- and period-prevalence estimates of anatomical regions of MSDs among Canadian firefighters. Methods: The MEDLINE, Embase, PubMed and Web of Science databases were searched from inception to November 2018. Cross-sectional cohort studies with musculoskeletal prevalence estimates (point- and period-) of career/professional firefighters in Canada were identified and critically appraised. MSDs were defined as sprains/strains, fractures/dislocations and self-reported bodily pain (chronic or acute). Period- and point-prevalence estimates were calculated, and study-specific estimates were pooled using a random-effects model. Results: Five eligible cohort studies (3 prospective, 2 retrospective) were included, with a total of 4,143 firefighters. The participants had a mean age range of 34 (SD = 8.5) to 42.6 (SD = 9.7) years. The reported types of MSDs included sprain or strain, fractures, head, neck, shoulder, elbow, arm, hand, back, upper thigh, knee, and foot pain. The point-prevalence estimate of shoulder pain was 23.00% (3 studies, 312 of 1,491 firefighters, 95% CI, 15.00–33.00), back pain was 27.0% (3 studies, 367 of 1,491 firefighters, 95% CI, 18.00–38.00), and knee pain was 27.00% (2 studies, 180 of 684 firefighters, 95% CI, 11.00–48.00). The one-year period-prevalence estimate of all sprain/strain injuries (all body parts) was 10.0% (2 studies, 278 of 2,652 firefighters, 95% CI, 7.00–14.00). Discussion: High point-prevalence estimates (1 in 4 firefighters) of shoulder-, back-, and knee-related MSDs were identified among Canadian firefighters. This emphasizes the need for early assessment, intervention, and injury prevention strategies that reflect how units work together to maximize ergonomic efficiency and injury prevention.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".