Evaluation of the McGill‐Tongji Blended Education Program for Teacher Leaders in General Practice: The importance of partnership and contextualization in International Primary Care Training Initiatives
Bibliographic record
Abstract
Purpose: Strong primary health care (PHC) systems require well-established PHC education systems to enhance the skills of general practitioners (GPs). However, the literature on the experiences of international collaboration in primary care education in low- and middle-income countries remains limited. The purpose of this study was to evaluate the implementation and perceived impact of the McGill-Tongji Blended Education Program for Teacher Leaders in General Practice (referred to as the "Tongji Program"). Methods: In 2020-2021, the McGill Department of Family Medicine (Montreal, Canada) and Tongji University School of Medicine (TUSM, Shanghai, China) jointly implemented the Tongji Program in Shanghai, China to improve the teaching capacity of PHC teachers. We conducted an exploratory longitudinal case study with a mixed methods design for the evaluation. Quantitative (QUAN) data was collected through questionnaire surveys and qualitative (QUAL) data was collected through focus group discussions. Results: The evaluation showed that learners in Tongji Program were primarily female GPs (21/22，95%) with less than 4 years of experience in teaching (16/22，73%). This program was considered a successful learning experience by most participants (19/22, 86%) with higher order learning tasks such as critical thinking and problem-solving. They also agreed that this program helped them feel more prepared to teach (21/22，95%), and developed a positive attitude toward primary care (21/22，95%). The QUAL interview revealed that both the Tongji and McGill organizers noted that TUSM showed strong leadership in organization, education, and coordination. Both students and teachers agreed that by adapting training content into contextualized delivery formats and settings, the Tongji Program successfully overcame language and technology barriers. Conclusions: Committed partnerships and contextualization were key to the success of the Tongji Program. Future research should focus on how international primary care education programs affect learners' behavior in their practice settings, and explore barriers and facilitators to change.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".