Association Between Musculoskeletal Injuries and the Canadian Armed Forces Physical Employment Standard Proxy in Canadian Military Recruits
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal injuries (MSKIs) and recruitment are major challenges faced by modern military forces. The Canadian Armed Forces uses a physical employment standard (PES) proxy to determine occupational fitness and job suitability. It is unknown whether the performance on the PES proxy can be also used as predictor of MSKIs. The purpose of this study was to investigate for relationships between age, sex, body composition, aerobic fitness, performance on the Canadian Armed Forces PES proxy (FORCE evaluation), and risk of sustaining a MSKI requiring intervention in the Training Rehabilitation Program (MSKI-TRP1) during Canadian Basic Military Qualification. MATERIALS AND METHODS: This was a retrospective analysis of MSKIs in recruits introduced in the Training Rehabilitation Program (TRP1) in 2016 and 2017. A two-tailed t-test and a multivariate stepwise logistic regression were completed to investigate the interrelationships of sex, age, FORCE performance (20 m rushes, sandbag lift, intermittent loaded shuttles, sandbag drag) and health-related characteristics (waist circumference, predicted peak oxygen consumption [$\dot{V}$O2peak]), and odds for sustaining a MSKI-TRP. RESULTS: The MSKI-TRP1 intervention rate observed was 4.3%. Rehabilitation duration was an average (SD) of 87 (76) days; nearly 80% of MSKI-TRP were lower body injuries. MSKI-TRP recruits were older, had a lower score on FORCE, and had a larger mean waist circumference and lower $\dot{V}$O2peak than non-TRP1 recruits (all P < 0.01). Recruits with performance lower than 1 SD below mean on the 20 m rushes, intermittent loaded shuttle, or sandbag drag were 2.69 (1.89-3.83), 2.74 (1.91-3.95), and 2.26 (1.52-3.37) times more likely to sustain a MSKI-TRP1, respectively (all P < 0.01). Recruits with $\dot{V}$O2peak lower than 1 SD below mean were also 2.19 (1.30-3.70) times more likely to sustain a MSKI-TRP. Neither sex, age, nor waist circumference impacted the risk of MSKI-TRP1 when controlling for FORCE performance. CONCLUSIONS: The Canadian Armed Forces PES proxy performance can be used to assess the odds of sustaining a MSKI-TRP1 in Canadian military recruit training.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".