Does a Canadian diabetes curriculum work for future physicians in China? Lessons from the Ottawa Shanghai Joint School of Medicine
Bibliographic record
Abstract
BACKGROUND: The Ottawa Shanghai Joint School of Medicine (OSJSM) is a campus of the University of Ottawa Medical School in Shanghai, China. This collaboration allowed us to study whether a Canadian curriculum is suitable for the Chinese population. The aim of this study is to evaluate: 1) The OSJSM diabetes curriculum; and 2) The relevancy of the content for the Chinese population. METHODS: The diabetes curriculum content was evaluated using a curriculum comparison between the University of Ottawa, OSJSM, and the Shanghai Jiao Tong School of Medicine (SJTSM). A literature search compared the diabetes populations in Canada and China. Interviews determined how physicians and patients manage diabetes. RESULTS: The diabetes curriculum at the OSJSM is identical to that of the University of Ottawa. Canada and China have a similar diabetes prevalence, diagnostic criteria, and management. Although both countries utilize the same screening guidelines for diabetes and its complications, patients in Canada are more likely to adhere to these recommendations. CONCLUSION: This study suggests that the diabetes content of the University of Ottawa curriculum remains relevant in China. A greater emphasis on the importance of screening for disease complications in the curriculum may facilitate making this a priority for patients and healthcare providers in China.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".