Sustainability of knowledge translation interventions in healthcare decision-making: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Knowledge translation (KT also known as research utilisation, translational medicine and implementation science) is a dynamic and iterative process that includes the synthesis, dissemination, exchange and ethically sound application of knowledge to improve health. After the implementation of KT interventions, their impact on relevant outcomes should be monitored. The objectives of this scoping review are to: (1) conduct a systematic search of the literature to identify the impact on healthcare outcomes beyond 1 year, or beyond the termination of funding of the initiative of KT interventions targeting chronic disease management for end-users including patients, clinicians, public health officials, health services managers and policy-makers; (2) identify factors that influence sustainability of effective KT interventions; (3) identify how sustained change from KT interventions should be measured; and (4) develop a framework for assessing sustainability of KT interventions. METHODS AND ANALYSIS: Comprehensive searches of relevant electronic databases (eg, MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials), websites of funding agencies and websites of healthcare provider organisations will be conducted to identify relevant material. We will include experimental, quasi-experimental and observational studies providing information on the sustainability of KT interventions targeting chronic disease management in adults and focusing on end-users including patients, clinicians, public health officials, health services managers and policy-makers. Two reviewers will pilot-test the screening criteria and data abstraction form. They will then screen all citations, full articles and abstract data in duplicate independently. The results of the scoping review will be synthesised descriptively and used to develop a framework to assess the sustainability of KT interventions. DISCUSSION AND DISSEMINATION: Our results will help inform end-users (ie, patients, clinicians, public health officials, health services managers and policy-makers) regarding the sustainability of KT interventions. Our dissemination plan includes publications, presentations, website posting and a stakeholder meeting.
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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 | 0.162 | 0.172 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.114 | 0.024 |
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".