“I felt like a little kind of jolt of energy in my chest”: embodiment in learning in continuing professional development for general practitioners
Bibliographic record
Abstract
Learning in medical education encompasses a broad spectrum of learning theories, and an embodiment perspective has recently begun to emerge in continuing professional development (CPD) for health professionals. However, empirical research into the experience of embodiment in learning in CPD is sparse, particularly in the practice of general medicine. In this study, we aimed to explore general practitioners' (GPs') learning experiences during CPD from an embodiment perspective, studying the appearance of elements of embodiment-the body, actions, emotions, cognition, and interactions with the surroundings and others-to build an explanatory structure of embodiment in learning. We drew on the concepts of embodied affectivity and mutual incorporation to frame our understanding of embodiment. Four Danish and three Canadian GPs were interviewed to gain insight into specific learning experiences; the interviews and the analysis were inspired by micro-phenomenology, augmented with a complex adaptive systems approach. We constructed an explanatory structure of learning with two entrance points (disharmony and mundanity), an eight-component learning phase, and an ending phase with two exit points (harmony and continuing imbalance). All components of the learning phase-community, pride, validation, rehearsal, do-ability, mind-space, ambiance, and preparing for the future-shared features of embodied affectivity and mutual incorporation and interacted in multi-directional and non-linear ways. We discuss integrating the embodiment perspective into existing learning theories and argue that CPD for GPs would benefit from doing so.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".