Virtual orientation for international medical graduates: A resident‐led initiative
Bibliographic record
Abstract
18% of trainees in the Internal Medicine (IM) programme at a large academic training programme are International Medical Graduates (IMGs). Previous scholarly work1 found that a major challenge for IMGs was navigating their initial months of residency. IMGs stated feelings of disorientation due to cultural and knowledge differences in the new academic setting, healthcare system and country. IMGs identified a need for mentorship and a desire to help other IMGs.1 This work informed the design of the current Longitudinal Collaborative IMG Mentorship Programme to facilitate the transition of IMGs into IM residency. Resident feedback also suggested that the orientation phase of the mentorship programme, which is usually delivered in the first 2 weeks of residency, occurred too late to mitigate disorientation during the first weeks of residency. It was hypothesised that moving orientation to a pre-residency stage would better prepare IMGs for the beginning of residency. The shift to virtual education during the COVID-19 pandemic provided this opportunity. The IMG Pre-Residency Orientation Curriculum was delivered as two 2-hour virtual sessions in June 2021 in the IM programme. Attendance was optional. The curriculum, which was based on an earlier needs assessment study,1 focussed on CanMEDS roles necessary for the first weeks of residency; Clinical Teaching Unit, roles in the interdisciplinary team, call, consulting and paging etiquette, verbal and written communication, study resources, formal assessment of IMG performance, wellness, and the medico-legal system in Canada. The curriculum was co-developed by senior IM IMG residents with supervision from the Faculty Lead of the IM IMG mentorship programme. The use of online software allowed for the editing and sharing of curricular content by multiple users longitudinally. Administrative support was provided by the IM programme. Participants were surveyed to evaluate the course. Overall, the course was a success. Ten residents attended, making up approximately 75% of the IMG Post-Graduate Year (PGY) 1 cohort. The participants who completed the survey all rated curriculum effectiveness as good or superior and strongly suggested its continuation in the future. The effectiveness of virtual teaching formats was a key learning point: (i) PGY 1 residents could attend the course regardless of their geographical location and timezone; (ii) sessions were recorded so that materials were available online for those who could not attend; (iii) the curriculum could be shared and edited online for future years. The resident-led nature of the sessions also contributed to success. Co-creation and delivery of curricular content by senior IMG residents reduced the burden of work for faculty in the IM programme. Senior IMG residents are well positioned to modify this curriculum given the recency of their experiences transitioning into residency as PGY1s. The evaluation of the curriculum should be improved. The survey was completed immediately after the course and had a low completion rate. Seeking feedback longitudinally during PGY1 would give further insight into the transition to residency for IMGs and promote curriculum improvement. This curriculum is now a regular part of the IMG mentorship programme.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".