The medical students’ perspective of faculty and informal mentors: a questionnaire study
Bibliographic record
Abstract
Abstract Background Student mentoring is an important aspect of undergraduate medical education. While medical schools often assign faculty advisors to medical students as mentors to support their educational experience, it is possible for the students to pursue mentors informally. The possible role of these informal mentors and their interactions with the students in a faculty mentorship program has not been reported. This study builds upon previous work that suggested many students have informal mentors, and that there might be interplay between these two types of mentors. This study was conducted to report the experience of undergraduate medical students in a faculty mentorship program of their faculty mentors and if applicable, of their informal mentors. Methods One month before residency (post-graduate training for Canadians) ranking, the survey was administered to the graduating class of 2014 at the University of Calgary’s Cumming School of Medicine. The survey was created from focus groups of the previous graduating class of 2013. The survey investigated meeting characteristics and the students’ perceptions of faculty advisors and informal mentors, and the students’ intended choice for residency. Results The study response rate was 86 % (95 of 111); 58 % (54 of 93) of the students reported having an informal mentor. There was no reported difference in satisfaction ratings of the Faculty mentorship program between students with only faculty mentors and those with also informal mentors. Students’ reporting of their satisfaction with the Facultymentorship program and the faculty mentors did not differ between the students with informal mentors and those with faculty mentors only. The students’ meeting frequency, discussed topics, and perceived characteristics of faculty mentors were not associated with having an informal mentor. The students generally perceived their informal mentors more positively than their faculty mentors. The reported student career intention was associated with the discipline of informal mentors and not with the discipline of faculty mentors. Conclusions Informal mentorship was common for medical students. The presence of an informal mentor was not associated with dissatisfaction with the Faculty advisor or with the mentorship program. It is likely students may pursue informal mentorship for career-related reasons.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".