Injury surveillance in the Canadian Armed Forces: An environmental scan
Bibliographic record
Abstract
LAY SUMMARY Injuries can affect the operational readiness, wellness, and careers of Canadian Armed Forces (CAF) personnel. Many injuries are preventable; thus, it is important for the CAF to create a sustainable, accurate, and timely injury surveillance system (ISS) that can be used to describe injury incidence, populations at risk, and other causal factors to effectively direct injury prevention efforts. As a first step in the creation of an ISS, the authors conducted a rapid environmental scan that included a review of both the peer-reviewed scientific literature and publicly available information, along with an internal organization scan, to gather information on ISS facilitators, barriers, recommendations, data sources, and potential injury indicators. The results of this work will be used to plan the next steps in the development and implementation of the CAF ISS. In addition, this information can be used to facilitate engagement and collaboration with stakeholders and decision makers to ensure that the ISS collects and reports key data needed to target and prioritize interventions most likely to have the greatest impact on reducing injuries and improving the health and operational readiness of CAF personnel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".