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Record W2131283562 · doi:10.3109/01421591003692730

Teaching professionalism across cultural and national borders: Lessons learned from an AMEE workshop

2010· article· en· W2131283562 on OpenAlexaff
Sylvia R. Cruess, Richard L. Cruess, Yvonne Steinert

Bibliographic record

VenueMedical Teacher · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: At the 2009 AMEE meeting in Malaga, Spain, a workshop was held entitled "Medicine's Social Contract with Society - An International Perspective." AIMS: One of the objectives of this workshop was to explore the international differences in medicine's social contract with society, which is linked to the nature of professionalism. METHODS: Nineteen individuals from 11 countries participated. They were asked to compare the expectations of the patients and the general public as well as those of the medical profession with those outlined in a recent publication addressing the issue from a North American perspective. RESULTS: The results indicated that the role of the healer appears to be universal, with only minor differences being found in different cultures and countries. However, there were differences noted in the role of the professional, which of course must reflect national differences in the social contract in health care. CONCLUSION: These differences must be respected by those teaching professionalism.

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 imitation

Not 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.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.253

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0070.007
Open science0.0050.017
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.126
GPT teacher head0.511
Teacher spread0.385 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations38
Published2010
Admission routes1
Has abstractyes

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