The Canada-Guyana medical education partnership: using videoconferencing to supplement post-graduate medical education among internal medicine trainees
Bibliographic record
Abstract
BACKGROUND: A Guyana-based, internal medicine (IM) post-graduate medical education program was established in 2013. However, lack of formal teaching sessions are barriers to the program's success. OBJECTIVE: To describe the partnership between the University of Calgary and the University of Guyana's internal medicine residency programs (IMRP). This partnership was created to support the Guyana's IM academic half-day and is characterized by mutually beneficial, resident-led videoconference teaching sessions. METHODS: Calgary medical residents volunteered to create and present weekly teaching presentations to Guyanese residents via videoconference. Questionnaires were completed by Guyanese residents and provided to Calgary residents as feedback on their teaching and presentation skills. A similar survey was completed by Calgary residents. LESSONS LEARNED: Twenty-four videoconference teaching sessions were conducted over eight months with a total of 191 and 16 surveys completed by Guyana and Calgary residents, respectively. Over 92% of both Guyana and Calgary residents agreed that the sessions enhanced their learning and over 93% reported increased interest in becoming more involved in international collaborations. 88% of Calgary residents felt the sessions improved their teaching skills. CONCLUSION: The formation of a resident-led, videoconference teaching series is a mutually beneficial partnership for Canadian and Guyanese medical residents and fosters international collaboration in medical education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".