Knowledge translation in surgery: a scoping review of implementation strategies, effectiveness and contextual barriers and enablers
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) interventions are essential for implementing evidence-based practices in healthcare. However, despite their proven effectiveness in addressing global health challenges, KT strategies in surgery remain challenging to apply. This scoping review examines KT strategies in surgery, their effectiveness, and key barriers and enablers to their implementation. METHODS: This scoping review followed the Arksey and O'Malley and Levac et al. frameworks, integrating the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the PRISM (Practical Robust Implementation Sustainability Model) models to evaluate the effectiveness of knowledge translation interventions in surgical practice change and associated contextual barriers and facilitators. A systematic search was conducted across MEDLINE (PubMed, OVID), CINAHL (EBSCO), and PsycINFO (ProQuest). Articles were screened using predefined selection criteria, emphasizing experimental and quasi-experimental studies. Data extraction categorized KT interventions: knowledge diffusion, dissemination, and implementation approaches. RESULTS: A total of 34 studies met the inclusion criteria. Most were hospital-based (88%) and focused on guideline adherence. The review identified three primary KT strategies: (i) educational materials and educational outreach, (ii) reminders and prompts, and (iii) audit and feedback systems. The most effective KT strategies used a combination of these interventions to maximize impact. Barriers included physician resistance, limited leadership support, financial constraints, and workflow disruptions, while enablers included institutional leadership, structured training programs, financial incentives, and interdisciplinary collaboration. A notable finding was the lack of standardized validation processes for adopting changes in the surgical setting, which often burdens individual surgeons and their institutions, thereby constraining both capacity and motivation for practice change. CONCLUSIONS: Findings suggest that layered, interdisciplinary KT strategies are the most effective for driving surgical practice change and overcoming institutional barriers. The integrated application of RE-AIM and PRISM frameworks proved valuable in assessing the interventions' sustainability and real-world effectiveness. This comprehensive analysis contributes to the growing body of knowledge on effective implementation strategies in surgical settings and provides a foundation for future practice improvement initiatives. Future research should focus on refining KT methodologies, expanding implementation frameworks, and addressing barriers to sustainability across diverse surgical settings.
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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 | 0.086 | 0.216 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.032 | 0.034 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".