Working Definitions of the Roles and an Organizational Structure in Health Professions Education Scholarship: Initiating an International Conversation
Bibliographic record
Abstract
PROBLEM: Health professions education scholarship (HPES) is an important and growing field of inquiry. Problematically, consistent use of terminology regarding the individual roles and organizational structures that are active in this field are lacking. This inconsistency impedes the transferability of current and future findings related to the roles and organizational structures of HPES. APPROACH: Based on data collected during interviews with HPES leaders in Canada, Australia, New Zealand, the United States, and the Netherlands, the authors constructed working definitions for some of the professional roles and an organizational structure that support HPES. All authors reviewed the definitions to ensure relevance across multiple countries. OUTCOMES: The authors define and offer illustrative examples of three professional roles in HPES (clinician educator, HPES research scientist, and HPES administrative leader) and an organizational structure that can support HPES participation (HPES unit). These working definitions are foundational and not all-encompassing and, thus, are offered as stimulus for international dialogue and understanding. NEXT STEPS: With these working definitions, scholars and administrative leaders can examine HPES roles and organizational structures across and between national contexts to decide how lessons learned in other contexts can be applied to their local contexts. Although rigorously constructed, these definitions need to be vetted by the international HPES community. The authors argue that these definitions are sufficiently transferable to support such scholarly investigation and debate.
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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 | 0.192 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.021 | 0.112 |
| Scholarly communication | 0.037 | 0.081 |
| Open science | 0.007 | 0.035 |
| Research integrity | 0.014 | 0.036 |
| 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".