The effectiveness of knowledge translation strategies used in public health: a systematic review
Bibliographic record
Abstract
BACKGROUND: Literature related to the effectiveness of knowledge translation (KT) strategies used in public health is lacking. The capacity to seek, analyze, and synthesize evidence-based information in public health is linked to greater success in making policy choices that have the best potential to yield positive outcomes for populations. The purpose of this systematic review is to identify the effectiveness of KT strategies used to promote evidence-informed decision making (EIDM) among public health decision makers. METHODS: A search strategy was developed to identify primary studies published between 2000-2010. Studies were obtained from multiple electronic databases (CINAHL, Medline, EMBASE, and the Cochrane Database of Systematic Reviews). Searches were supplemented by hand searching and checking the reference lists of included articles. Two independent review authors screened studies for relevance, assessed methodological quality of relevant studies, and extracted data from studies using standardized tools. RESULTS: After removal of duplicates, the search identified 64, 391 titles related to KT strategies. Following title and abstract review, 346 publications were deemed potentially relevant, of which 5 met all relevance criteria on full text screen. The included publications were of moderate quality and consisted of five primary studies (four randomized controlled trials and one interrupted time series analysis). Results were synthesized narratively. Simple or single KT strategies were shown in some circumstances to be as effective as complex, multifaceted ones when changing practice including tailored and targeted messaging. Multifaceted KT strategies led to changes in knowledge but not practice. Knowledge translation strategies shown to be less effective were passive and included access to registries of pre-processed research evidence or print materials. While knowledge brokering did not have a significant effect generally, results suggested that it did have a positive effect on those organizations that at baseline perceived their organization to place little value on evidence-informed decision making. CONCLUSIONS: No singular KT strategy was shown to be effective in all contexts. Conclusions about interventions cannot be taken on their own without considering the characteristics of the knowledge that was being transferred, providers, participants and organizations.
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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 | 0.105 | 0.326 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".