Family medicine curriculum: improving the quality of academic sessions.
Bibliographic record
Abstract
UNLABELLED: PROBLEM ADDRESSED The Family Medicine Residency Program at the University of Alberta has used academic sessions and clinical-based teaching to prepare residents for private practice. Before the new curriculum, academic sessions were large group lectures given by specialists. These sessions lacked consistent quality, structured topics, and organization. OBJECTIVE OF PROGRAM: The program was designed to improve the quality and consistency of academic sessions by creating a new curriculum. The goals for the new curriculum included improved organizational structure, improved satisfaction from the participants, improved resident knowledge and confidence in key areas of family medicine, and improved performance on licensing examinations. PROGRAM DESCRIPTION: The new curriculum is faculty guided but resident organized. Twenty-three core topics in family medicine are covered during a 2-year rotating curriculum. Several small group activities, including problem-based learning modules, journal club, and examination preparation sessions, complement larger didactic sessions. A multiple-source evaluation process is an essential component of this new program. CONCLUSION: The new academic curriculum for family medicine residents is based on a variety of learning styles and is consistent with the principles of adult learning theory. This structured curriculum provides a good basis for further development. Other programs across the country might want to incorporate these ideas into their current programming.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".