Academic half-day curricula in Canadian general internal medicine programs: A descriptive study
Bibliographic record
Abstract
Introduction: Canadian general internal medicine (GIM) subspecialty programs offer classroom-based teaching through academic half days (AHDs) to supplement clinical teaching. It is unclear how GIM programs can optimize AHDs to support trainees with heterogenous career paths, as nationwide guidance on AHD implementation is lacking. Methods: All 13 English-speaking Canadian GIM programs were invited to participate in a short-answer survey assessing 6 aspects of AHD. Two years of AHD topics from each program were extracted from provided curricular calendars and sorted into 10 categories. Results: The response rate was 100%. The assessed aspects of AHD, including the medium of delivery, total curricula time, frequency of AHD, and delegation of teaching and scheduling responsibilities differed across programs. Regarding the average total curricular time, most was spent on medical knowledge acquisition (30%) and self-directed time (35%). Most schools incorporated sessions on transition to practice ( n = 13/13; 100%) and evidence-based medicine ( n = 11/13; 85%). Fewer schools had simulation sessions ( n = 5/13; 38%) and point-of-care ultrasound teaching ( n = 5/13; 38%). Discussion: Classroom-based teaching is heterogeneously implemented across English-speaking Canadian GIM programs. Future studies exploring GIM trainees’ perceptions of AHDs’ purpose and effectiveness could provide valuable insights into how programs can best support their learners.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".