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The impact of a modern medical curriculum on students' proposed behaviour on meeting ethical dilemmas

2004· article· en· W2112981779 on OpenAlexaff
John Goldie, Lisa Schwartz, Alex McConnachie, Jillian Morrison

Bibliographic record

VenueMedical Education · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCurriculumMedical educationEngineering ethicsPsychologyMedicinePedagogyEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the impact of a modern medical curriculum on students' proposed behaviour on encountering ethical dilemmas. DESIGN: Cohort design. SETTING: University of Glasgow Medical School. SUBJECTS: The first intake of students into Glasgow's new curriculum (n = 238). Main outcome measure Student answers consistent with consensus professional judgement on the ethical dilemmas posed by the vignettes of the Ethics and Health Care Survey Instrument. RESULTS: The probability of giving a consensus answer was lowest pre-Year 1 and highest post-Year 1. It reduced slightly post-Years 3 and 5, but remained significantly higher than at pre-Year 1. The performance of students undertaking a 1-year intercalated BSc, however, appeared to regress on testing post-Year 4. CONCLUSIONS: While the first year of the curriculum had a positive impact on students, the remainder of the curriculum did not impact to the same extent. These findings support the recommendation that small group teaching, the predominant teaching method in Year 1, should be preferred to lecture and large group teaching, the predominant method of the remaining curricular years. Full integration of ethics and law teaching within the rest of the curriculum is recommended, particularly during the clinical years. This has training implications for all medical teachers involved in the curriculum. The assessment of ethics should be incorporated into all formal examinations. It is recommended that ethics be addressed as part of a wider approach to professionalism in order to promote integration.

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)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.407
Teacher spread0.396 · 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 designObservational
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

Citations57
Published2004
Admission routes1
Has abstractyes

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