Using local injury surveillance for community-based injury prevention: an analysis of Scandinavian WHO Safe Community and Canadian Safe Community Foundation programmes
Bibliographic record
Abstract
Injury surveillance is widely recognized as a critical prerequisite for effective injury prevention, yet few studies have investigated its use by community-based injury prevention programmes. This study examined the extent to which local injury data were collected, documented, analysed, linked to injury prevention action and used for evaluation among WHO Safe Communities in Scandinavia (25 programmes) and the Canadian Safe Community Foundation (SCF) network (16 programmes). For each programme, a key informant with relevant local knowledge was selected to respond to an emailed questionnaire. The study demonstrates that community-based injury prevention programmes experience difficulties accessing and effectively utilizing local injury surveillance data. The findings suggest that the responding SCF programmes approach injury prevention more scientifically than the Scandinavian WHO-designated Safe Community programmes, by making greater use of injury surveillance for assessment, integration into prevention strategies and measures, and evaluation. Despite study limitations, such as the low response rate among Canadian programmes and a large number of non-responses to two questions, the results highlight the importance of, and need for, greater use of local injury surveillance.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".