Evaluating the “McMUST” global learning partnership: resident insights on knowledge exchange to enhance learning in postgraduate medical education
Bibliographic record
Abstract
BACKGROUND: To critically evaluate a global learning partnership called "McMUST," which was initiated collaboratively between Mbarara University of Science and Technology (MUST) in Uganda and McMaster University in Canada in 2014. METHODS: A combination of quantitative and qualitative data collection methods was used. Evaluation forms were used to collect satisfaction and learning experience data from Canadian and Ugandan psychiatry residents during eight of 11 visits to Uganda by Canadian faculty and residents. The visits occurred between 2015 and 2023 and involved Canadian faculty and residents collaborating with local counterparts in psychiatry at MUST. Quantitative data were analyzed using means and standard deviations, while qualitative comments underwent conventional content analysis. RESULTS: Satisfaction ratings from 56 evaluations out of a total possible of 62 were consistently high across all visits (Range = 3.83-5.00 / 5.00, M = 4.52, SD = 0.41). Qualitative findings revealed five themes: (1) Enriched learning, highlighting the transformative experience for residents in challenging existing perspectives; (2) Effective pedagogy, emphasizing the value of diverse learning strategies; (3) Navigating cross-cultural and professional roles, focusing on Canadian residents' transformative learning journeys; (4) Patient experiences-Humanizing psychiatric education, underscoring a shared focus on humanistic patient care; and (5) Enhancing future visits, addressing challenges and suggesting improvements, such as extending visit durations, supporting ongoing connections between residents, and advocating for bidirectional travel. CONCLUSIONS: The consistently high satisfaction ratings across multiple visits indicate that the global learning partnership between MUST and McMaster University has been successful in providing an enriching learning experience for residents participating in collaborative clinical work and learning of psychiatry in Uganda. Despite many positive findings, our partnership was not immune to some of the equity-related problems that have been documented in the literature. Going forward, advocacy efforts to gather resources that will allow for bidirectional travel for residents will be essential. Our findings also highlight opportunities to evaluate impact longitudinally, especially on participants' clinical practice and patient outcomes.
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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 | 0.049 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".