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Record W2065735687 · doi:10.1080/01421590500314207

EBM user and practitioner models for graduate medical education: what do residents prefer?

2006· article· en· W2065735687 on OpenAlexaff
Elie A. Akl, Nancy Maroun, Gabriela Neagoe, Gordon Guyatt, Holger J. Schünemann

Bibliographic record

VenueMedical Teacher · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCurriculumMedical educationCritical appraisalSession (web analytics)Graduate medical educationMedicinePsychologyFamily medicineAlternative medicinePedagogyComputer science

Abstract

fetched live from OpenAlex

The objective of the study reported in this article was to assess and explain medical residents' preferences for the evidence based medicine (EBM) practitioner versus the EBM user models. A self-administered survey and focus group of residents attending a core curriculum EBM master session were undertaken. Most residents, particularly those earlier in their training, preferred the practitioner model. Residents perceived that model as an opportunity to gain advanced EBM skills during residency, as providing the ability to choose practicing under both models, and as offering the gain of independent thinking and greater self-confidence in their critical appraisal skills. The user model had the advantage of reduced time requirements. In sum, the majority of residents preferred a curriculum that focuses on the practitioner over the user model.

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)0.006
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.212
GPT teacher head0.522
Teacher spread0.310 · 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 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

Citations18
Published2006
Admission routes1
Has abstractyes

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