Knowledge translation strategies to improve the use of evidence in public health decision making in local government: intervention design and implementation plan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Knowledge translation strategies are an approach to increase the use of evidence within policy and practice decision-making contexts. In clinical and health service contexts, knowledge translation strategies have focused on individual behavior change, however the multi-system context of public health requires a multi-level, multi-strategy approach. This paper describes the design of and implementation plan for a knowledge translation intervention for public health decision making in local government. METHODS: Four preliminary research studies contributed findings to the design of the intervention: a systematic review of knowledge translation intervention effectiveness research, a scoping study of knowledge translation perspectives and relevant theory literature, a survey of the local government public health workforce, and a study of the use of evidence-informed decision-making for public health in local government. A logic model was then developed to represent the putative pathways between intervention inputs, processes, and outcomes operating between individual-, organizational-, and system-level strategies. This formed the basis of the intervention plan. RESULTS: The systematic and scoping reviews identified that effective and promising strategies to increase access to research evidence require an integrated intervention of skill development, access to a knowledge broker, resources and tools for evidence-informed decision making, and networking for information sharing. Interviews and survey analysis suggested that the intervention needs to operate at individual and organizational levels, comprising workforce development, access to evidence, and regular contact with a knowledge broker to increase access to intervention evidence; develop skills in appraisal and integration of evidence; strengthen networks; and explore organizational factors to build organizational cultures receptive to embedding evidence in practice. The logic model incorporated these inputs and strategies with a set of outcomes to measure the intervention's effectiveness based on the theoretical frameworks, evaluation studies, and decision-maker experiences. CONCLUSION: Documenting the design of and implementation plan for this knowledge translation intervention provides a transparent, theoretical, and practical approach to a complex intervention. It provides significant insights into how practitioners might engage with evidence in public health decision making. While this intervention model was designed for the local government context, it is likely to be applicable and generalizable across sectors and settings. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Register ACTRN12609000953235.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it