P.118 Curriculum mapping can facilitate transition to Competence by Design
Bibliographic record
Abstract
Background: Curriculum maps outline the content of an educational program identifying links between targeted outcomes, educational opportunities, and assessments. The transition to Competence by Design (CBD) in Canadian specialty residency programs requires thoughtful reorganization of educational programming. A curriculum map may assist with understanding the existing curriculum and thereby facilitate planning for CBD. Methods: A map of the pediatric neurology residency curriculum at the University of Calgary was constructed by linking objectives with related learning activities and assessments. Qualitative line-by-line analysis was then conducted to identify gaps in the existing curriculum. The map was used as a framework to plot CBD outcomes and curricular structure as these were established. Results: Generating the traditional curriculum map was time-consuming, requiring 48 hours. Careful review identified several objectives that did not link to formal learning activities or assessments. Many such gaps were recognized to link to non-clinical activities. Using the scaffold of the traditional curriculum reduced the time required for mapping the planned CBD curriculum to 4 hours. Conclusions: The creation of a curriculum map prior to transition to CBD improved understanding of the existing curriculum and will facilitate transition to CBD. Ongoing evaluation of the fit of our predicted CBD map will support effective implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".