Student learning experiences in a longitudinal clerkship programme
Bibliographic record
Abstract
OBJECTIVE: To identify how medical student learning experiences in a new longitudinally integrated clinical clerkship (LICC) programme impacted students' learning. METHODS: We conducted semi-structured interviews with 12 medical students at three points in their training. We used an inductive, thematic analytic approach to data. Interviews (n = 35) were iteratively and independently coded by research team members to identify and corroborate key emergent themes. RESULTS: Students in the LICC programme reported slow but ongoing increases in patient responsibility, examination-driven learning, programme flexibility to address educational gaps, and a strong and positive perception of educational continuity through a longitudinal primary care educator and similar case mix throughout the year. CONCLUSIONS: Student learning experiences in an LICC programme are both similar to and different from those in a traditional rotational clerkship programme. Students in the integrated clerkship were clear and unequivocal about the benefits of working with one teacher across time and caring for patients at different stages of the same disease in multiple settings. These findings have implications for clinical education development and design.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".