What do medical students actually do on clinical rotations?
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".