Prevalence of musculoskeletal symptoms among Canadian firefighters
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal symptoms (MSSs) remain the most frequently reported type of injuries sustained during fire-ground operations in firefighters. However, there is a paucity of reports concerning the prevalence estimates of MSSs among female firefighters and different fire services across Canada. OBJECTIVES: To assess the point prevalence of self-reported MSSs, stratified by age and sex in a cohort of active duty firefighters from across Canada, and to determine whether age, sex or length of service can be used to predict the likelihood of the number of MSSs sustained. METHODS: We recruited 390 firefighters (272 males, 118 females). To identify the prevalence of self-reported rates of MSSs, firefighters were asked to complete a standardized 11-item questionnaire that asked, "Please indicate whether you have experienced pain in any body region within the last week", with response options that included "Yes", "No", and "Head", "Neck", "Shoulder", "Arm/Elbow/Hand", "Back", "Stomach/Abdomen", "Upper Thigh", "Knee", "Lower Leg", "Foot", "Other, please specify". RESULTS: Among the 390 full-time firefighters, 212 (54%) indicated to have experienced some type of MSSs within the last week. The most prevalent region-specific MSSs included, 123 (32%) in the back region, 92 (24%) in the shoulder region, 74 (19%) in the neck region and 70 (18%) in the knee region. In addition, women indicated a 1.6 times greater likelihood of sustaining ≥2 MSSs when controlling for individual differences in age and years of service. CONCLUSIONS: The point prevalence of MSSs in a cohort of full-time firefighters was 54% (55% males; 53% females). Women experienced a 1.4-1.6 times greater likelihood of sustaining MSSs when controlling for individual differences in age and years of service.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".