A typology of longitudinal integrated clerkships
Bibliographic record
Abstract
CONTEXT: Longitudinal integrated clerkships (LICs) represent a model of the structural redesign of clinical education that is growing in the USA, Canada, Australia and South Africa. By contrast with time-limited traditional block rotations, medical students in LICs provide comprehensive care of patients and populations in continuing learning relationships over time and across disciplines and venues. The evidence base for LICs reveals transformational professional and workforce outcomes derived from a number of small institution-specific studies. OBJECTIVES: This study is the first from an international collaborative formed to study the processes and outcomes of LICs across multiple institutions in different countries. It aims to establish a baseline reference typology to inform further research in this field. METHODS: Data on all LIC and LIC-like programmes known to the members of the international Consortium of Longitudinal Integrated Clerkships were collected using a survey tool developed through a Delphi process and subsequently analysed. Data were collected from 54 programmes, 44 medical schools, seven countries and over 15 000 student-years of LIC-like curricula. RESULTS: Wide variation in programme length, student numbers, health care settings and principal supervision was found. Three distinct typological programme clusters were identified and named according to programme length and discipline coverage: Comprehensive LICs; Blended LICs, and LIC-like Amalgamative Clerkships. Two major approaches emerged in terms of the sizes of communities and types of clinical supervision. These referred to programmes based in smaller communities with mainly family physicians or general practitioners as clinical supervisors, and those in more urban settings in which subspecialists were more prevalent. CONCLUSIONS: Three distinct LIC clusters are classified. These provide a foundational reference point for future studies on the processes and outcomes of LICs. The study also exemplifies a collaborative approach to medical education research that focuses on typology rather than on individual programme or context.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".