Effect of a community oriented problem based learning curriculum on quality of primary care delivered by graduates: historical cohort comparison study
Bibliographic record
Abstract
OBJECTIVE: To assess whether the transition from a traditional curriculum to a community oriented problem based learning curriculum at Sherbrooke University is associated with the expected improvements in preventive care and continuity of care without a decline in diagnosis and management of disease. DESIGN: Historical cohort comparison study. SETTING: Sherbrooke University and three traditional medical schools in Quebec, Canada. PARTICIPANTS: 751 doctors from four graduation cohorts (1988-91); three before the transition to community based problem based learning (n = 600) and one after the transition (n = 151). OUTCOME MEASURES: Annual performance in preventive care (mammography screening rate), continuity of care, diagnosis (difference in prescribing rates for specific diseases and relief of symptoms), and management (prescribing rate for contraindicated drugs) assessed using provincial health databases for the first 4-7 years of practice. RESULTS: After transition to a community oriented problem based learning curriculum, graduates of Sherbrooke University showed a statistically significant improvement in mammography screening rates (55 more women screened per 1000, 95% confidence interval 10.6 to 99.3) and continuity of care (3.3% more visits coordinated by the doctor, 0.9% to 5.8%) compared with graduates of a traditional medical curriculum. Indicators of diagnostic and management performance did not show the hypothesised decline. Sherbrooke graduates showed a significant fourfold increase in disease specific prescribing rates compared with prescribing for symptom relief after the transition. CONCLUSION: Transition to a community oriented problem based learning curriculum was associated with significant improvements in preventive care and continuity of care and an improvement in indicators of diagnostic performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".