A peer mentoring initiative across medical residency programs
Bibliographic record
Abstract
Purpose Several studies have shown benefits of peer mentoring on wellness among medical students and health care professionals. Peer mentorship has also been pointed as having interesting potential for International Medical Graduates. However, the literature on peer mentoring at the residency level is very limited. The purpose of this paper is to assess the benefits of a resident-led pilot peer-mentoring initiative at McGill University. Design/methodology/approach Over 2 years, 17 residents from various residency programs were put in contact with a volunteer peer mentor by e-mail. The structure of the mentorship was flexible. A survey using Likert scale and free text responses was sent to all the participants. Findings There were response rates of 65 percent for mentees and 59 percent for mentors. The majority of mentees thought the service was either moderately helpful (18 percent) or helpful (36 percent). Several residents noted that communication by e-mails and lack of in-person contacts were a limitation in the mentorship experience. The most frequent challenge that led to consult the service was immigration or arrival from another province. Originality/value The results show that the program can be helpful to medical residents, is cost-effective, flexible and could be adapted and replicated elsewhere. In the future, the program will adjust to tend toward a more structured frame, highlighting the importance of in-person contacts. The small sample size of participants and the recall bias are some limitations of our study.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".