MétaCan
Menu
Back to cohort
Record W2096749131 · doi:10.1002/chp.1340240305

Hidden curriculum in continuing medical education

2004· article· en· W2096749131 on OpenAlexaff
Nancy L. Bennett, Jocelyn Lockyer, Karen Mann, Helen Batty, Karen Laforet, Jan‐Joost Rethans, Ivan Silver

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSt. Michael's HospitalCARE CanadaUniversity of Toronto
Fundersnot available
KeywordsCurriculumHidden curriculumGraduation (instrument)Medical educationContinuing medical educationCertificationMedicineContinuing educationPsychologyPolitical sciencePedagogyEngineering

Abstract

fetched live from OpenAlex

In developing curricula for undergraduate and graduate medical education, educators have become increasingly aware of an interweaving of the formal, informal, and hidden curricula and their influences on the outcomes of teaching and learning. But, to date, there is little in the literature about the hidden curriculum of medical practice, which takes place after graduation and certification. This article initiates that discussion with influences of the hidden curriculum on the actions physicians take or do not take in caring for patients. Hafferty's framework of institutional policies, evaluation activities, resource-allocation decisions, and institutional slang, along with our knowledge of health services research and the continuing medical education (CME) research literature, suggests that there is a hidden and powerful curriculum that affects physician performance. Determining whether the hidden curriculum conflicts with the messages that we are delivering through formal CME (courses, clinical practice guidelines, peer review journals) may contribute to improving our impact on physician performance.

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)0.006
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.424
Teacher spread0.408 · 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.

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

Citations54
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Continuing Education in the Health ProfessionsSame topicInnovations in Medical EducationFrench-language works237,207