The effectiveness of toolkits as knowledge translation strategies for integrating evidence into clinical care: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The aim of this systematic review was to evaluate the effectiveness of toolkits as a knowledge translation (KT) strategy for facilitating the implementation of evidence into clinical care. Toolkits include multiple resources for educating and/or facilitating behaviour change. DESIGN: Systematic review of the literature on toolkits. METHODS: A search was conducted on MEDLINE, EMBASE, PsycINFO and CINAHL. Studies were included if they evaluated the effectiveness of a toolkit to support the integration of evidence into clinical care, and if the KT goal(s) of the study were to inform, share knowledge, build awareness, change practice, change behaviour, and/or clinical outcomes in healthcare settings, inform policy, or to commercialise an innovation. Screening of studies, assessment of methodological quality and data extraction for the included studies were conducted by at least two reviewers. RESULTS: 39 relevant studies were included for full review; 8 were rated as moderate to strong methodologically with clinical outcomes that could be somewhat attributed to the toolkit. Three of the eight studies evaluated the toolkit as a single KT intervention, while five embedded the toolkit into a multistrategy intervention. Six of the eight toolkits were partially or mostly effective in changing clinical outcomes and six studies reported on implementation outcomes. The types of resources embedded within toolkits varied but included predominantly educational materials. CONCLUSIONS: Future toolkits should be informed by high-quality evidence and theory, and should be evaluated using rigorous study designs to explain the factors underlying their effectiveness and successful implementation.
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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 | 0.092 | 0.277 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| 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".