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Record W2057994134 · doi:10.1080/01421590701758665

The wounding path to becoming healers: medical students’ apprenticeship experiences

2008· article· en· W2057994134 on OpenAlexaffabout
Dawn Allen, Megan Wainwright, Balfour M. Mount, Tom A. Hutchinson

Bibliographic record

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsApprenticeshipPath (computing)Medical educationCareer pathMedicinePsychologyComputer scienceEngineeringHistoryEngineering management

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.067
GPT teacher head0.391
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations47
Published2008
Admission routes2
Has abstractyes

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