Harnessing a knowledge translation framework to implement an undergraduate medical education intervention: A longitudinal study
Bibliographic record
Abstract
INTRODUCTION: Implementation of evidence-informed educational interventions (EEI) involves applying and adapting theoretical and scientific knowledge to a specific context. Knowledge translation (KT) approaches can both facilitate and structure the process. The purpose of this paper is to describe lessons learned from applying a KT approach to help implement an EEI for clinical reasoning in medical students. METHODS: Using the Knowledge to Action framework, we designed and implemented an EEI intended to support the development of students' clinical reasoning skills in a renewed medical curriculum. Using mixed-methods design, we monitored students' engagement with the EEI longitudinally through a platform log; we conducted focus groups with students and stakeholders, and observed the unfolding of the implementation and its continuation. Data are reported according to six implementation outcomes: Fidelity, Feasibility, Appropriateness, Acceptability, Adoption, and Penetration. RESULTS: Students spent a mean of 24 min on the activity (fidelity outcome) with a high completion rate (between 75% and 95%; feasibility outcome) of the entire activity each time it was done. Focus group data from students and stakeholders suggest that the activity was acceptable, appropriate, feasible, adopted and well-integrated into the curriculum. DISCUSSION: Through the process we observed the importance of having a structuring framework, of working closely and deliberatively with stakeholders and students, of building upon concurrent evaluations in order to adapt iteratively the EEI to the local context and, while taking students' needs into consideration, of upholding the EEI's core educational principles.
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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 | 0.048 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".