Navigating the research landscape: How paradigms shape health professions education research
Bibliographic record
Abstract
CONTEXT: The rich and varied landscape of Health Profession Education (HPE) research includes many different approaches to research practice, often reflecting different research paradigms and different ontological, epistemological and axiological positions. The coexistence of different approaches to research practice and the valuing of interdisciplinary research means those conducting research in HPE must not only be able to situate their work within this landscape but also have an appreciation of similarities and differences across research practices in order to conduct or engage with interdisciplinary scholarship. To support HPE scholars and researchers in navigating the interdisciplinary HPE research landscape, we provide an overview of six paradigms used in HPE research and provide several means through which to compare and contrast their attributes. METHOD: Using a metaphor of mapping the HPE research landscape, we present three maps through which to examine the similarities, differences and areas of overlap across six key paradigms in HPE research. Focusing on the ontological, epistemological and axiological elements of these different paradigms, we provide an opportunity for readers to consider these paradigms concurrently. DISCUSSION: These three means of mapping can be reflective aids for those engaging in HPE research; allowing for a nuanced consideration of ontological, epistemological and axiological position for a given research practice. Having an understanding of research practices and approaches that span multiple paradigms can help support individual scholars to situate their work within the HPE landscape and help research teams engaging in interdisciplinary research navigate important paradigmatic differences. We hope that these maps provide tools and terminology to better navigate research landscapes while recognizing that maps can never accurately reflect the full complexity, nuance and detail of the territory.
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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 | 0.131 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.026 | 0.100 |
| Scholarly communication | 0.058 | 0.060 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.008 | 0.011 |
| 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; 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".