Caffeinate a resident: Brewing career connections
Bibliographic record
Abstract
Mentoring relationships aimed at medical students can enhance educational satisfaction and support career development.1 Most interactions with postgraduate medical education (PGME) colleagues are limited to senior clerkship years, leaving pre-clinical students with minimal mentorship opportunities early on. This reality is often present in other health care fields where clinical practicums are integrated into the later years of training. This early gap in mentorship may limit guidance early on. We launched the Caffeinate a Resident programme in 2018 as part of McGill University's Medical Student Society, pairing medical students with residents in their preferred specialties through informal coffee or tea meetings, helping facilitate mentorship opportunities between colleagues in undergraduate medical education (UGME) and PGME. Students rank their specialty preferences and are matched with volunteering residents. Since its inception, the programme has connected a total of 776 students with 729 residents. Each year, an average of 28 PGME programmes participate (SD: 5.8). The University of British Columbia Medical Undergraduate Society has since implemented a similar programme. Caffeinate a Resident distinguishes itself by providing early informal and relaxed mentorship between trainees following similar footsteps. Since 2018, we have streamlined the program with no-cost automation via free educational institutional licences (Power Automate, Microsoft Forms, Excel; Microsoft Corporation, USA), requiring about 10 hours to automate and operate annually from two dedicated volunteers. Beverages are independently arranged and purchased by the students and residents. As a result, the programme has been designed to operate at zero cost. The program follows a five-step process: stakeholder engagement, participant online registration, match database creation, communication of pairings and post-program online survey evaluation. Since its inception in 2018, the Caffeinate a Resident programme has revealed four key takeaways as they relate to (1) efficiency and sustainability, (2) programme engagement, (3) adaptability and (4) organizational culture. First, we discovered that automation is valuable in educational innovations, minimizing human error and significantly reducing time and effort requirements. A programme that initially involved dozens of hours of commitment over weeks can now be completed in 1 week, ensuring less burden on the programme's student leaders. Second, we learned that to ensure student and resident engagement, the programme must ally itself with existing institutions, such as student and resident associations, programme directors and the UGME and PGME offices. When all stakeholders were involved early, we had a 1.5-fold increase in PGME programmes participating in 2023 (26 to 39 programmes). Third, we learned that such a programme can be adaptable to other training contexts outside our own, namely, at the University of British Columbia. This initiative has the potential to be adapted and successfully integrated into other allied health programmes. Lastly, we learned through informal feedback amongst our peers that implementing such mentorship dyads is an important step towards fostering a greater sense of belonging amongst the UGME and PGME through low-stakes and informal settings, an impact reflected by the sustained engagement and annual participation of both trainees and PGME programmes. Sarah Moussa: Conceptualization; writing—original draft; writing—review and editing; project administration; resources; visualization; supervision. Nehal Islam: Writing—original draft; writing—review and editing; project administration; resources. Aurgho Datta: Writing—original draft; writing—review and editing; project administration; resources. We thank past and present Ambassadors for Comprehensive Education (ACE) sub-committee of the McGill Medical Student Society and all McGill resident doctors who have participated in this programme since 2018. Special thanks to the Association of McGill Residents (ARM), the Postgraduate Medical Education Dean and McGill's programme directors for their invaluable support. None. Not applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".