Twelve tips for designing and running longitudinal integrated clerkships
Bibliographic record
Abstract
Longitudinal integrated clerkships (LICs) involve learners spending an extended time in a clinical setting (or a variety of interlinked clinical settings) where their clinical learning opportunities are interwoven through continuities of patient contact and care, continuities of assessment and supervision, and continuities of clinical and cultural learning. Our twelve tips are grounded in the lived experiences of designing, implementing, maintaining, and evaluating LICs, and in the extant literature on LICs. We consider: general issues (anticipated benefits and challenges associated with starting and running an LIC); logistical issues (how long each longitudinal experience should last, where it will take place, the number of learners who can be accommodated); and integration issues (how the LIC interfaces with the rest of the program, and the need for evaluation that aligns with the dynamics of the LIC model). Although this paper is primarily aimed at those who are considering setting up an LIC in their own institutions or who are already running an LIC we also offer our recommendations as a reflection on the broader dynamics of medical education and on the priorities and issues we all face in designing and running educational programs.
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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 | 0.101 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".