Affordances of Knowledge Translation in Medical Education
Bibliographic record
Abstract
PURPOSE: Little is known about knowledge translation processes within medical education. Specifically, there is scant research on how and whether faculty incorporate empirical medical education knowledge into their educational practices. The authors use the conceptual framework of affordances to examine factors within the medical education practice environment that influence faculty utilization of empirical knowledge. METHOD: In 2012, the authors, using a purposive sampling strategy, recruited medical education leaders in undergraduate medical education from a Canadian university. Recruits all had direct teaching and curricular development roles in either preclinical or clinical courses across the four years of the undergraduate curriculum. Data were collected through individual semistructured interviews on participants' use of empirical evidence, as well as the factors that influence integration of empirical knowledge into practice. Data were analyzed using thematic analysis. RESULTS: Fifteen medical educators participated. The authors identified both constraining and facilitating affordances of empirical medical education knowledge use. Constraining affordances included poor quality and availability of evidence, inadequate knowledge delivery approaches, work and role overload, faculty and student change resistance, and resource limitations. Facilitating affordances included faculty development, peer recommendations, and local involvement in medical education knowledge creation. CONCLUSIONS: Affordances of the medical education practice environment influence empirical knowledge use. Developing strategies for effective knowledge translation thus requires careful assessment of contextual factors that can enable, constrain, or inhibit evidence use. Empirical knowledge use is most likely to occur among medical educators who are afforded rich, facilitative opportunities for participation in creating, seeking, and implementing knowledge.
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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 | 0.050 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".