Rethinking implementation science for health professions education: A manifesto for change
Bibliographic record
Abstract
Implementation science approaches the challenges of translating evidence into practice as a matter of scientific inquiry. This conceptual paper uses an implementation science lens to examine the ways in which evidence from health professions education research is brought to bear on decision-making. The authors describe different decision-making contexts and the kinds of evidence they consider, and from this, they outline ways in which research findings might be better presented to support their translation into policy and practice. Reflecting on the nature of decision-making in health professions education and how decisions are made and then implemented in different health professions education contexts, the authors argue that researchers should align their work with the decision-making contexts that are most likely to make use of them. These recommendations reflect implementation science principles of packaging and disseminating evidence in ways that are meaningful for key stakeholders, that stem from co-creation of knowledge, that require or result in meaningful partnerships, and that are context specific and relevant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.266 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.044 | 0.039 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 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".