The nature of evidence resources and knowledge translation for health promotion practitioners
Bibliographic record
Abstract
Governments and other public health agencies have become increasingly interested in evidence-informed policy and practice. Translating research evidence into programmatic change has proved challenging and the evidence around how to effectively promote and facilitate this process is still relatively limited. This paper presents the findings from an evaluation of a series of evidence-based health promotion resources commissioned by the Victorian Department of Human Services. The evaluation used qualitative methods to explore how practitioners for whom the resources were intended, viewed and used them. Document and literature review and analysis, and a series of key informant interviews and focus groups were conducted. The findings clearly demonstrate that the resources are unlikely to act as agents for change unless they are linked to a knowledge management process that includes practitioner engagement. This paper also considers the potential role of knowledge brokers in helping to identify and translate evidence into practice.
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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 | 0.335 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.052 | 0.049 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".