Key factors associated with the high burden of injuries in the Canadian forces
Bibliographic record
Abstract
Background Military populations are particularly vulnerable to injury due the nature of their work and the required levels of physical training. The Canadian Forces Health and Lifestyle Information Survey (HLIS) is a regularly conducted population health survey on health status, risk factors and demographics from a stratified random sample of Canadian Forces (CF) personnel. The 2008/9 HLIS indicated that in the preceding year 23% of Canadian Forces (CF) personnel had sustained an activity limiting repetitive strain injury (RSI) and 21% an activity limiting acute injury. Among CF personnel unable to deploy, 32% identified musculoskeletal injury as the reason. Methods HLIS sampling methods are described in detail elsewhere. The 2008/2009 version of the HLIS included more detailed questions about risk taking behaviours, specific types of physical training activities, and reasons CF members were unable to deploy for active duty. There were three separate outcomes of interest, acute injury, RSI and deployment prohibitive musculoskeletal injury. Multivariate logistic regression models were developed to investigate which injury risk factors remained significant when adjusted for other parameters in the model. Findings Bivariate analysis identified several military and physical training variables significantly associated with acute, RSI and deployment prohibitive injuries. Multivariate logistic regression revealed more complex relationships; the association between training and injury was mediated by the inclusion of lifestyle and demographic variables in the models. This exploratory approach provides a more comprehensive description of injuries leading to more effective surveillance and prevention planning in the CF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".