Body pedagogics: embodied learning for the health professions
Bibliographic record
Abstract
MEDICINE AS EMBODIED PRACTICE: Bodily dysfunctions bring patients to their doctors and even diseases of the mind can originate in patients' bodies. Doctors respond by using their own bodies - hands, eyes, ears and sometimes noses - to make diagnoses and treat diseases. Yet, despite the embodied nature of practice, medicine typically treats the body as an object, paying scant attention to the subjective embodied experiences of patients and doctors. Much health professions education (HPE) reflects this, prioritising cognition over learners' sense of embodiment. Hence there is a gap between the embodied realities of practice and the disembodied nature of medical education. This article introduces readers to 'body pedagogics' as a framework that can help to re-establish embodiment as a central principle of HPE. BODY PEDAGOGICS: This embodiment theory, drawn from sociology, anthropology and phenomenology, has informed such disparate fields as glassblowing education and military training. Body pedagogics emphasises learning as a physically embodied process. It illustrates how multisensory experience causes embodied changes that become an automatic part of physician expertise. We introduce core body pedagogic concepts using physical examination as an example, examining the bodily means of HPE, students' bodily experiences and the resulting bodily changes. IMPLICATIONS: Body pedagogics can help us to focus attention on embodiment as a central principle of HPE that transcends the discipline-specific teaching of clinical skills. Moreover, it provides a set of conceptual foundations for an interdisciplinary practice within HPE with implications for instructional design. Body pedagogics can also help us to make strange the habits and disregarded aspects of embodied learning and in so doing help us to consider embodiment more critically and directly in practice and education, and in the ways we research them.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".