A public health approach to mobilizing community partners for injury prevention: A scoping review
Bibliographic record
Abstract
OBJECTIVES: Reducing injuries in adults requires work with diverse stakeholders across many sectors and at multiple levels. At the local level, public health professionals need to effectively bring together, facilitate, and support community partners to initiate evidence-based efforts. However, there has been no formal review of the literature to inform how these professionals can best create action among community partners to address injuries in adults. Thus, this scoping review aims to identify theories, models or frameworks that are applicable to a community-based approach to injury prevention. METHODS: Searches of scientific and less formal literature identified 13,756 relevant items published in the English language between 2000 and 2016 in North America, Europe and Australia. After screening and review, 10 publications were included that (1) identified a theory, framework or model related to mobilizing partners; and (2) referred to community-based adult injury prevention. RESULTS: Findings show that use of theories, frameworks and models in community-based injury prevention programs is rare and often undocumented. One theory and various conceptual models and frameworks exist for mobilizing partners to jointly prevent injuries; however, there are few evaluations of the processes to create community action. CONCLUSIONS: Successful community-based injury prevention must build on what is already understood about creating partnership action. Evaluating local public health professional injury prevention practice based on available theories, models and frameworks will identify successes and challenges to inform process improvements. We propose a logic model to more specifically guide and evaluate how public health can work locally with community partners.
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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 | 0.032 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".