Knowledge translation strategies to support the sustainability of evidence-based interventions in healthcare: a scoping review
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) strategies are widely used to facilitate the implementation of EBIs into healthcare practices. However, it is unknown what and how KT strategies are used to facilitate the sustainability of EBIs in institutional healthcare settings. OBJECTIVES: This scoping review aimed to consolidate the current evidence on (i) what and how KT strategies are being used for the sustainability of EBIs in institutional healthcare settings; (ii) the reported KT strategy outcomes (e.g., acceptability) for EBI sustainability, and (iii) the reported EBI sustainability outcomes (e.g., EBI activities or component of the intervention continue). METHODS: We conducted a scoping review of five electronic databases. We included studies describing the use of specific KT strategies to facilitate the sustainability of EBIs (more than 1-year post-implementation). We coded KT strategies using the clustered ERIC taxonomy and AIMD framework, we coded KT strategy outcomes using Tierney et al.'s measures, and EBI sustainability outcomes using Scheirer and Dearing's and Lennox's taxonomy. We conducted descriptive numerical summaries and a narrative synthesis to analyze the results. RESULTS: The search identified 3776 studies for review. Following the screening, 25 studies (reported in 27 papers due to two companion reports) met the final inclusion criteria. Most studies used multi-component KT strategies for EBI sustainability (n = 24). The most common ERIC KT strategy clusters were to train and educate stakeholders (n = 38) and develop stakeholder interrelationships (n = 34). Education was the most widely used KT strategy (n = 17). Many studies (n = 11) did not clearly report whether they used different or the same KT strategies between EBI implementation and sustainability. Seven studies adapted KT strategies from implementation to sustainability efforts. Only two studies reported using a new KT strategy for EBI sustainability. The most reported KT strategy outcomes were acceptability (n = 10), sustainability (n = 5); and adoption (n = 4). The most commonly measured EBI sustainability outcome was the continuation of EBI activities or components (n = 23), followed by continued benefits for patients, staff, and stakeholders (n = 22). CONCLUSIONS: Our review provides insight into a conceptual problem where initial EBI implementation and sustainability are considered as two discrete time periods. Our findings show we need to consider EBI implementation and sustainability as a continuum and design and select KT strategies with this in mind. Our review has emphasized areas that require further research (e.g., KT strategy adaptation for EBI sustainability). To advance understanding of how to employ KT strategies for EBI sustainability, we recommend clearly reporting the dose, frequency, adaptations, fidelity, and cost of KT strategies. Advancing our understanding in this area would facilitate better design, selection, tailored, and adapted use of KT strategies for EBI sustainability, thereby contributing to improved patient, provider, and health system outcomes.
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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 | 0.132 | 0.323 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.048 | 0.036 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".