“A lot of it is about feel”: The promise of sensory ethnography for anatomical education research
Bibliographic record
Abstract
Ethnographers have constructed rich accounts of cultural settings since the early nineteenth century. A new approach, sensory ethnography, holds great promise for Health Professions Education scholars in its incorporation of the senses, particularly regarding anatomical teaching and learning. In this article, we describe sensory ethnography as a promising approach for anatomical sciences education research. We draw on our sensory ethnographic research on human donor learning programs to provide concrete examples of this approach in action, in all its complexity and promise. We explore how the senses can be woven into key phases of the research process and describe challenges and considerations we grappled with during our research. Finally, drawing on our research data, we offer five key ways sensory ethnography can elevate our understanding of Health Professions Education.
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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 | 0.057 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".