Development of an injury surveillance system for the Canadian forces
Bibliographic record
Abstract
Injuries represent a leading cause of morbidity and mortality in the Canadian military. The 2008/2009 Health and Lifestyle Information Survey (HLIS) found that in the preceding 12 months 23% of Canadian Forces (CF) personnel had sustained a repetitive strain injury and 21% an acute injury. These injuries were mainly attributed to physical training/sports/Adventure training. CF occupational fitness requirements necessitate participation in vigorous physical training, sports and military exercises, placing this population at increased risk of non-battle related injury, with adverse implications for operational readiness. The 2008/2009 HLIS found that among CF personnel unable to deploy, 32% identified musculoskeletal injury as the reason. To obtain more detailed information about injury incidence, trends and risk factors required to plan locally-driven prevention activities, an injury surveillance pilot system is being implemented in Canadian Forces Base Valcartier, Quebec. The system is based on the Australian Defence Force and Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) models where detailed information about injuries is collected at the point of medical contact. This paper presents the challenges in implementing injury surveillance systems in a clinical environment which has geographically dispersed health clinics, non-standardised patient triage processes, high pre-existing workloads and a variety of military stakeholders. Despite these potential barriers, the Canadian Department of National Defence Injury surveillance project team has developed an innovative and promising system for injury data collection, and expects that results from the analysis of the data collection process to be available by autumn 2010.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".