How can we support best practice? A situational assessment of injury prevention practice in public health
Bibliographic record
Abstract
BACKGROUND: To effectively impact the significant population burden of injury, we completed a situational assessment of injury prevention practice within a provincial public health system to identify system-wide priorities for capacity-building to advance injury prevention in public health. METHODS: A descriptive qualitative study was used to collect data on the current practice, challenges and needs of support for injury prevention. Data was collected through semi-structured interviews (n = 20) and focus groups (n = 19). Participants included a cross-section of injury prevention practitioners and leadership from public health units reflecting different population sizes and geographic characteristics, in addition to public health researchers and experts from academia, public health and not-for-profit organizations. Thematic analysis was used to code all of the data by one reviewer, followed by a second independent reviewer who coded a random selection of interview notes. Major codes and sub codes were identified and final themes were decided through iterations of coding comparisons and categorization. Once data were analysed, we confirmed the findings with the field, in addition to participating in a prioritization exercise to surface the top three needs for support. RESULTS: Major themes that were identified from the data included: current public health practice challenges; capacity and resource constraints, and; injury as a low priority area. Overall, injury prevention is a broad, complex topic that competes with other areas of public health. Best practices are challenged by system-wide factors related to resources, direction, coordination, collaboration, and emerging injury public health issues. Injury is a reportedly under prioritized and under resourced public health area of practice. Practitioners believe that increasing access to data and evidence, and improving collaboration and networking is required to promote best practice. CONCLUSIONS: The results of this study suggest that there are several system level needs to support best practice in public health injury prevention in Ontario including reducing research to practice gaps and supporting opportunities for collaboration. Our research contributes to the literature of the complexity of public health practice, and presents several mechanisms of support to increase capacity at a system level to improve injury prevention practice, and eventually lessen the population burden of injury.
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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 | 0.136 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".