Whose problem is it anyway? Confronting myths of ‘problems’ in health professions education
Bibliographic record
Abstract
INTRODUCTION: The growing interest in knowledge translation and implementation science, both in clinical practice and in health professions education (HPE), is reflected in the number of studies that have sought to address what are believed to be evidence-practice gaps. Though this effort may be intended to ensure practice improvements are better aligned with research evidence, there is a common assumption that the problems researchers explore and the answers they generate are meaningful and applicable to practitioner needs. METHODS: This Mythology paper considers the nature of problems from HPE as the focus of HPE research and the ways in which they may or may not be aligned. The authors argue that, in an applied field such as HPE, it is vital that researchers better understand how their research problems relate to practitioner needs and what the limitations on evidence uptake might be. Not only can this establish clearer paths between evidence and action, but it also requires a rethink of much of knowledge translation and implementation science thinking and practice. RESULTS: The authors explore five myths: whether everything in HPE is a problem; whether practitioner needs involve problem solving; whether practitioner problems are resolvable with sufficient evidence; whether researchers effectively target practitioner problems; and whether studies that focus on solving practitioner problems make significant contributions to the literature. CONCLUSIONS: To advance the conversation on the connections between problems and HPE research, the authors propose ways in which knowledge translation and implementation science might be approached differently.
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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 | 0.163 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.016 | 0.222 |
| Scholarly communication | 0.034 | 0.069 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.026 | 0.062 |
| Insufficient payload (model declined to judge) | 0.003 | 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".