Sex disparities in self-reported musculoskeletal injuries in the Canadian Armed Forces
Bibliographic record
Abstract
Recent comprehensive systematic reviews indicate that females are at greater risk of musculoskeletal injuries (MSKi) than males in military populations. Considering the Canadian Armed Forces (CAF) goal of increasing female representation in the next few years, exploring these trends is essential. We aimed to determine the association between biological sex and MSKi in the CAF. An online survey was conducted with active-duty and former CAF members aged 18–65 years. Sex disparities in MSKi (acute or repetitive strain [RSI]) were analyzed using bivariate associations and binary logistic regressions with significance level at p < 0.05. Analyses were stratified by military environment (i.e., Army, Navy, and Air Force). From the 1947 respondents whose biological sex was reported, 855 were females and 1092 were males. Rates of RSI sustained by females and males while serving were 76.2% and 70.5% ( p = 0.011), respectively, whereas 61.4% of females reported acute injuries compared to 63.7% of males ( p = 0.346). Females were more likely to report overall RSI (adjusted odds ratio [aOR]: 1.397; 95% confidence intervals [CI]: 1.068–1.829), RSI having a greater impact on daily activities (aOR [95%CI]: 2.979 [2.093–4.239]) and greater impact on career progress/length (aOR [95%CI]: 1.448 [1.066–1.968]). Acute injuries, also more prevalent in females, were reported to have a greater impact on daily activities (aOR [95%CI]: 1.688 [1.198–2.379]). This study highlights sex disparities in MSKi prevalence and outcomes. Females within the CAF sample presented greater likelihood of reporting RSI, perceived impact of RSI on daily activities and career progress/length, and perceived impact of acute injuries on daily activities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".