Real time curriculum map for internal medicine residency
Bibliographic record
Abstract
BACKGROUND: To manage the voluminous formal curriculum content in a limited amount of structured teaching time, we describe the development and evaluation of a curriculum map for academic half days (AHD) in a core internal medicine residency program. METHODS: We created a 3-year cyclical curriculum map (an educational tool combining the content, methodology and timetabling of structured teaching), comprising a matrix of topics under various specialties/themes and corresponding AHD hours. All topics were cross-matched against the ACP-ASIM in-training examination, and all hours were colour coded based on the categories of core competencies. Residents regularly updated the map on a real time basis. RESULTS: There were 208 topics covered in 283 AHD hours. All topics represented core competencies with minimal duplication (78% covered once in 3 years). Only 42 hours (15%) involved non-didactic teaching, which increased after implementation of the map (18-19 hours/year versus baseline 5 hours/year). Most AHD hours (78%) focused on medical expert competencies. Resident satisfaction (90% response) was high throughout (range 3.64 +/- 0.21, 3.84 +/- 0.14 out of 4), which improved after 1 year but returned to baseline after 2 years. CONCLUSION: We developed and implemented an internal medicine curriculum map based on real time resident input, with minimal topic duplication and high resident satisfaction. The map provided an opportunity to balance didactic versus non-didactic teaching, and teaching on medical versus non medical expert topics.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".