Setting up and operationalizing a health professions education research (HPER) unit: AMEE Guide No.170
Bibliographic record
Abstract
In the same way as clinical medicine, health professions education should be evidence-based rather than based on tradition and convenience. Health professions education research (HPER), an academic area that first emerged in the 1950s, is essential for identifying new and better ways to educate health professionals. Again, just as with clinical research, setting up sustainable HPER units is critical to coordinate research efforts and facilitate the production of clear and strategic HPER. In this AMEE guide we draw upon the scholarly and grey literature and our own experiences as HPER unit leaders in several different global contexts to provide practical guidance on establishing and sustaining a HPER unit. We outline the multiple elements and considerations required to set up and operationalize a successful HPER unit, from engagement of key stakeholders and documentation of milestones to the production of programmatic research and its implementation. These are considered under the areas of • Who do you need to partner with? • Setting the agenda - or What will your unit be known for? • Your most valuable resource - people! • Operationalizing your HPER agenda • Leading the way We provide concrete tips on each of the above and illustrate these key steps with examples from our own experiences or the wider literature. Whether the reader is beginning, maintaining, or seeking to renew their HPER unit, we hope that the guidance we provide is as useful as it has been to us during our own research program building endeavours.
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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 | 0.062 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.036 | 0.055 |
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".