Taking a definitional stance in health professions education scholarship
Bibliographic record
Abstract
PROBLEM: Definitions are fundamental to the work of scholarship. Indeed, all scholarship has a definitional stance, even if that stance is not to use definitions. A definitional stance is the position scholars take regarding the use, interpretation or treatment of definitions within their work. In this paper, the authors explore definitional stances that shape inquiry in health professions education (HPE), from the formulation of research questions to the interpretation and dissemination of findings. Despite their ambient presence, definitional stances are rarely acknowledged in scholarly work, nor are they explicitly and consistently examined in peer review processes, critical appraisal, the methodological literature or in graduate education. As a result, definitional ambiguity and misalignment often goes unnoticed, and the coherence of scholarly discourse is undermined. DEFINITIONAL STANCES: The authors describe eight distinct types of definitional stances taken in health professions education. These range from adefinitional (avoiding any definitions) and rhetorical (adopting definitions as tools of persuasion) positions that resist fixed meanings, to realist, construct and pattern-based stances that embrace definitional coherence while allowing for ongoing inquiry and conceptual evolution. The authors illustrate the utility of this framework through a worked example (using the construct of professionalism), showing how different stances yield different understandings and scholarly pathways. WHAT THIS PAPER ADDS: There is no one 'right' definitional stance, but rather to promote thoughtfulness, reflexivity and transparency in how definition stances are taken and the implications thereof. The paper offers practical guidance to help scholars identify, articulate and justify their definitional stances in ways that are aligned with their epistemological commitments and research purposes. By making definitional stances more deliberate, transparent and open to discussion, HPE scholarship can make stronger knowledge claims based on a richer understanding of the kinds of knowledge that different stances afford, which has the potential to advance HPE in both principled and pragmatic ways.
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 | 0.210 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.024 | 0.204 |
| Scholarly communication | 0.045 | 0.061 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.018 | 0.043 |
| Insufficient payload (model declined to judge) | 0.004 | 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".