Group-based mentoring in undergraduate medical education
Bibliographic record
Abstract
This thesis focuses on group mentorship for undergraduate medical students as a pivotal element in fostering a supportive and collaborative learning environment, essential for the complex field of medicine. Previous studies have mostly focused on one-on-one mentoring. Group mentorships can foster a collaborative and reflective environment in which students can benefit from the collective knowledge and experiences of their peers and mentors. In Paper I, a systematic review was conducted to identify group mentorships for medical students. Based on the findings, we provide insights for structuring and assessing such mentorships. We highlight the benefits of making such programs compulsory, longitudinal, and integrated with the curriculum, along with mentor support and frequent evaluations. Paper II explores group mentors’ perceptions at three universities in Norway and Canada, specifically what factors influence their level of satisfaction. The main results were that physician mentors’ overall satisfaction is closely linked to them experiencing fulfilling mentor–student relationships and personal and professional development. Paper III investigates the UiT medical students’ experiences and attitudes by comparing the first class of students with a longitudinal mentorship program and the final class of students in the old curriculum without such a program. The findings suggest that a longitudinal group-based mentorship program can make students feel better prepared for clinical practice and help them develop positive attitudes toward important professional attributes such as patient-centeredness. In conclusion, the findings from the three papers emphasize the significant potential of group mentoring programs in medical education. Investigating group mentorship is essential not only for understanding its immediate impact on students’ academic and professional growth but also for its potential implications on the culture of medical education and practice.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".