Same but Different: Exploring Mechanisms of Learning in a Longitudinal Integrated Clerkship
Bibliographic record
Abstract
PURPOSE: Longitudinal integrated clerkships (LICs) are a widely used method of delivering clerkship curricula. Although there is evidence that LICs work and core components of LIC training have been identified, there is insufficient understanding of which components are integral to why they work. To address this question, this research explored how students experienced the first year of an LIC program. The aim was to use participants' understanding of their learning experiences to identify potential mechanisms of the LIC curriculum model. METHOD: Thirty-two interviews were conducted with 13 University of Toronto students, 7 LIC and 6 block rotation students from the same site, from October 2014 to September 2015. A thematic analysis was performed iteratively to explore participants' understanding of their key learning experiences and outcomes. RESULTS: Participants in both cohorts described their key learning outcome as integration and application of knowledge during patient care. Experiences supporting this outcome were articulated as longitudinal variable practice and continuity of relationships with preceptors and patients. Critically, these experiences manifested differently for the 2 cohorts. For block students, these learning experiences appeared to reflect the informal curriculum, whereas for LIC students, learning experiences were better supported by the LIC formal curriculum. CONCLUSIONS: The results illustrate the importance of learning experiences that support longitudinality and continuity. By also emphasizing variability and knowledge integration, they align with literature on expert development. Notably, many of the learning experiences identified resulted from informal learning and thus support going beyond the formal curriculum when evaluating the effectiveness of curricula.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".