Mentorship Programs in Residency: A Scoping Review
Bibliographic record
Abstract
Background: Mentorship during residency training is correlated with improved outcomes. Many residency programs have implemented formal mentorship programs; however, reported data for these programs have not been previously synthesized. Thus, existing programs may fall short on delivering effective mentorship. Objective: To synthesize current literature on formal mentorship programs in residency training in Canada and the United States, including program structure, outcomes, and evaluation. Methods: In December 2019, the authors performed a scoping review of the literature in Ovid MEDLINE and Embase. The search strategy included keywords relevant to mentorship and residency training. Eligibility criteria included any study describing a formal mentorship program for resident physicians within Canada or the United States. Data from each study were extracted in parallel by 2 team members and reconciled. Results: A total of 6567 articles were identified through the database search, and 55 studies met inclusion criteria and underwent data extraction and analysis. Though reported program characteristics were heterogenous, programs most commonly assigned a staff physician mentor to a resident mentee with meetings occurring every 3 to 6 months. The most common evaluation strategy was a satisfaction survey at a single time point. Few studies performed qualitative evaluations or used evaluation tools appropriate to the stated objectives. Analysis of data from qualitative studies allowed us to identify key barriers and facilitators for successful mentorship programs. Conclusions: While most programs did not utilize rigorous evaluation strategies, data from qualitative studies provided insights into barriers and facilitators of successful mentorship programs, which can inform program design and improvement.
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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 | 0.025 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".