The wounding path to becoming healers: medical students’ apprenticeship experiences
Bibliographic record
Abstract
BACKGROUND: This article responds to repeated calls in the literature to teach medical students how to treat the whole patient, not just the disease. It focuses on the educational experiences of medical students in a Canadian university in an effort to clarify the determinants of "caring" in medical education. METHOD: Nineteen (19) second-year medical students volunteered to keep weekly journal entries during the first five months of their medical apprenticeship. In journal entry analyses, the authors identified themes through a consensus-building coding process detailed in the work of Maykut and Morehouse (1994) and Huckin (2004). For this article, the authors focus on those themes most closely related to the students' caring experiences during their apprenticeship. RESULTS: The data highlight components of the medical system which made it difficult for students to engage in caring practices during their apprenticeship: the competing discourses of empathy and efficiency, the objectification of patients, the power of the medical hierarchy, and the institutionalized practice of wounding. CONCLUSION: The authors argue that returning medical care and students' experience to a balance of attention to curing and caring is a complex undertaking requiring a re-conceptualization of the process and goals of medical care.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".