Near-peer mentorship for newly qualified doctors; what are the benefits, and what methods can be used to overcome the pitfalls?
Bibliographic record
Abstract
INTRODUCTION: Access to mentorship is frequently cited as a priority for doctors, however formal mentorship programmes throughout training are lacking for residents. Near-peer mentorship is a faculty-light option to potentially bridge this mentorship gap, however the literature in the clinical postgraduate setting is not comprehensive. We aim to evaluate the benefits and pitfalls of near-peer mentorship in a postgraduate setting. MATERIALS AND METHODS: of near-peer mentorship were applied. After allocation to the frameworks' overarching themes, data was analysed thematically. RESULTS: Across 10 identified studies (one quantitative, three qualitative, six mixed-methods), near-peer mentorship was perceived to be beneficial (72-100% approval), with 85-99% of mentors and mentees desiring continuation of schemes at their units (seven UK-based, two Australian, one Canadian). The main themes identified were Mentee benefits, including careers advice and development of transferable skills; Mentor benefits, including leadership and organisational skill improvements; Organisational benefits, including reduced faculty workload and an enhanced sense of community. Pitfalls included a perceived lack of mentor expertise, shortage of time and resources, and unsupportive mentoring relationships. DISCUSSION: This research suggests that near-peer mentorship offers benefits for the mentee, mentor and organisation, if care is taken to mitigate the potential pitfalls. The main benefits versus traditional senior faculty mentorship derive from the concept of social and cognitive congruence, whereby a more closely relatable tutor is better able to tailor learning in terms understood by the tutee. Practical recommendations to optimise near-pear mentorship include mentorship pyramids, matching of pairings within the same clinical sites, and a hybrid matching approach with the option for mentee-led selection.
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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 | 0.104 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".