Curricular Monikers: What's in a Name?
Bibliographic record
Abstract
This article was migrated. The article was not marked as recommended. Introduction: An increasing number of North American medical schools are assigning unique names ("monikers") to their undergraduate curricula, but it is unclear as to how often this occurs, and what kind of names schools are choosing. Method: A manual review of the 160 websites that corresponded to Schools of Medicine that were either fully or provisionally accredited by the Liaison Committee on Medical Education (LCME). Results: 31.5% of the 143 U.S. allopathic medical schools and only one (5.8%) of the 17 LCME accredited Canadian medical schools currently associate a unique curricular name with their undergraduate medical education programs. Use of a constant-comparative technique suggested that schools that did assign a curricular name to their programs had selected names that aligned to one of eight over-arching themes. Conclusions: While curricular names were somewhat less commonly applied in schools located in the western United States, no specific trends in thematic choices predominated in any geographic region. However, the impact of curricular themes on current and/or prospective medical students remains an area for continued exploration, as does the longitudinal question as to whether thematically named curricula succeed in delivering their intended results.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".