Bibliographic record
Abstract
In UK medical schools there is a growing interest in a new model of undergraduate medical education; the Longitudinal Integrated Clerkship (LIC). In this model, the central principles are continuity, integration, and longitudinality;1,2 students participate in ‘the comprehensive care of patients over time’,3 continuing learning relationships with patients’ clinicians, and meet the majority of the core curricular competencies across multiple disciplines simultaneously.1 They do this by focusing on patients rather than morbidity categorised by specialty. The model grew out of initiatives to address rural medical workforce shortages in the US in the 1970s and spread during the 1980s to Australia, Canada, and South Africa. There is now a variety of different models worldwide with a median duration of 40 weeks4 and most are based in primary care. In a LIC, students follow a group of patients through episodes of care wherever they take place. Their initial encounters with these patients take place in a variety of settings throughout the clerkships; some in primary care, some in emergency departments or acute assessment units, and some in outpatient clinics. Educational supervisors help the students develop a diverse patient group so that their learning is broad and meets the requirements of the curriculum. LIC students perform at least as well and often better than those in more traditional curricula. Their consultation skills are well-developed, they understand more about the psychosocial aspects of medicine, take on more responsibility for patients and have more confidence in dealing with ethical issues.5 Continuity of relationships with clinical teachers and patients is consistently quoted as the reason why LICs are effective in promoting learning.6 Participation in the care of patients over time has benefits for students as a result of the social aspects of having a role7,8 …
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".