Peer mentoring in medical residency education: A systematic review
Bibliographic record
Abstract
BACKGROUND: Medical residents may experience burnout during their training, and a lack of social support. This can impact their overall wellbeing and ability to master key professional competencies. We explored, in this study, the extent to which peer mentorship promotes psychosocial wellbeing and the development of professional competencies in medical residency education. METHODS: We searched six databases (MEDLINE, EMBASE, PsycINFO, Academic Research Complete, ERIC, Education Research Complete) for studies on peer mentoring relationships in medical residency. We selected any study where authors reported on outcomes associated with peer mentoring relationships among medical residents. We applied no date, language, or study design limits to this review. RESULTS: We included nine studies in this systematic review. We found that medical residents received essential psychosocial supports from peers, and motivation to develop academic and career competencies. Medical residents in peer-mentoring relationships also reported increased overall satisfaction with their residency training programs. CONCLUSIONS: Peer-mentoring relationships can enhance the development of key professional competencies and coping mechanisms in medical residency education. Further rigorous research is needed to examine the comparative benefits of informal and formal peer mentoring, and identify best practices with respect to effective design of peer-mentorship programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".