Unmasking the divide: Musculoskeletal injury and physical fitness disparities among military and emergency responders
Bibliographic record
Abstract
Introduction: Musculoskeletal injuries (MSKi) are a burden on military, first responder, and health care personnel. Military and emergency services populations are often grouped together in research, but direct comparison of physical fitness or injuries of these groups has not been conducted for females. Moreover, interactions among parity status, occupation, and injury history with respect to physical fitness have not been explored. Methods: Fifty-seven female firefighters, paramedics, law enforcement officers, or health care providers (non-military [NM]) and 90 female Canadian Armed Forces (CAF) members completed testing for flexibility (sit and reach), muscular power (standing long jump and medicine ball throw), muscular strength (back squat and bench press), muscular endurance (Biering-Sorenson test, single-leg wall sit, and push-ups), and aerobic capacity (graded treadmill maximal oxygen uptake test). Results: Likelihood ratios indicated the CAF group had more injuries involving the back, hip, foot, and lumbopelvic hip complex, whereas the NM group had more thumb injuries. One-, two-, and three-way analysis of variance identified multiple interactions among MSKi, occupation (military vs. NM), and parity status with respect to physical fitness, with the NM group performing better on multiple physical test components. Discussion: Physical fitness disparities and differences in body regions injured suggest that CAF and NM populations are less comparable than traditionally thought. Moreover, parity status appears to play a role in physical fitness for individuals employed in these occupations. Initiatives and research to support females in arduous careers should consider parity status and injury history while differentiating between military and NM populations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".