MétaCan
Menu
Back to cohort
Record W2153862250 · doi:10.1080/01421590412331285397

What do medical students actually do on clinical rotations?

2004· article· en· W2153862250 on OpenAlexaff
Paul Worley, David Prideaux, Roger Strasser, Robyn March, Elizabeth Worley

Bibliographic record

VenueMedical Teacher · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsNOSM University
FundersFlinders University
KeywordsMedical educationMedicineReferralClinical clerkshipCommunity hospitalAcademic yearNursingFamily medicinePsychologyCurriculumMathematics educationPedagogy

Abstract

fetched live from OpenAlex

As medical schools make use of an increasing variety of clinical teaching settings, it is of interest to find that that there is very little published research that explores the actual learning activities undertaken by students in different environments. This study was designed to describe and analyse a typical week for students learning the same curricular material in one of three Australian settings: an urban tertiary teaching hospital, a remote secondary referral hospital and a rural community-based programme. Twenty-eight students completed week-long learning logs in weeks 9 and 35 of a 40-week academic year. Each student recorded his or her activity in 15-minute intervals for each week. Analysis of these data revealed that, compared with the hospital-based students, the community-based students reported greater patient contact, more time spent in clinical settings and increased time supervised by experienced clinicians. Whilst the community-based students valued their learning in clinical settings more highly than the learning they undertook at their home, the opposite was found for the tertiary hospital-based students. This study, the first to compare student activity in these three prototypical settings in the medical education literature, provides empirical evidence supporting community-based programmes as credible alternatives to traditional teaching hospital-based environments.

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)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0240.002

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 head0.051
GPT teacher head0.472
Teacher spread0.421 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations44
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueMedical TeacherSame topicInnovations in Medical EducationFrench-language works237,207