The applicability of generalisability and bias to health professions education's research
Bibliographic record
Abstract
CONTEXT: Research in health professions education (HPE) spans an array of topics and draws from a diversity of research domains , which brings richness to our understanding of complex phenomena and challenges us to appreciate different approaches to studying them. To fully appreciate and benefit from this diversity, scholars in HPE must be savvy to the hallmarks of rigour that differ across research approaches. In the absence of such recognition, the valuable contributions of many high-quality studies risk being undermined. METHODS: In this article, we delve into two constructs---generalisability and bias--that are commonly invoked in discussions of rigour in health professions education research. We inspect the meaning and applicability of these constructs to research conducted from different paradigms (i.e., positivist and constructivist) and orientations (i.e., objectivist and subjectivist) and then describe how scholars can demonstrate rigour when these constructs do not align with the assumptions underpinning their research. CONCLUSIONS: A one-size-fits-all approach to evaluating the rigour of HPE research disadvantages some approaches and threatens to reduce the diversity of research in our field. Generalisability and bias are two examples of problematic constructs within paradigms that embrace subjectivity; others are equally problematic. As a way forward, we encourage HPE scholars to inspect their assumptions about the nature and purpose of research-both to defend research rigour in their own studies and to ensure they apply standards of rigour that align with research they read and review.
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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 | 0.829 | 0.897 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.010 | 0.130 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.011 | 0.030 |
| Research integrity | 0.021 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".