Cocreating a programme to prevent injuries and improve performance in Australian Police Force recruits: a study protocol
Bibliographic record
Abstract
A healthy police force is a key component of a well-functioning society, yet 1 in 20 law enforcement recruits drop out of the recruit training programme due to injury. This drop-out rate has substantial economic and workforce ramifications. In the Western Australia Police Force, one in five recruits suffers a musculoskeletal injury during the recruit training programme, causing time-loss from work. We will now identify the critical elements of an injury prevention intervention and investigate the needs, experiences and suggested solutions to address potential implementation challenges. Our objective is to co-create an intervention with content and context experts, specifically for Western Australia Police Force recruits, to reduce injury prevalence, incidence rates and burden. A mixed-method participatory action research approach will guide intervention cocreation. Phase 1 will include concept mapping and phase 2 will include focus groups. This research will develop an intervention that the Western Australia Police Force can deliver to reduce injury prevalence, incidence rates and burden among recruits. The effectiveness of the intervention in reducing injury burden, economic burden and implementation will be evaluated.
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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 | 0.046 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.051 | 0.013 |
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".