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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".