How to match mentors and protégés for successful mentorship programs: a review of the evidence and recommendations for practitioners
Bibliographic record
Abstract
Purpose The purpose of this qualitative review paper is to identify for practitioners ways of matching mentors and protégés to enhance the effectiveness of formal mentoring programs. Design/methodology/approach The paper qualitatively reviews the best available evidence of ways to match mentors and protégés to maximize mentorship outcomes. Findings Two factors to consider when making mentor–protégé matches emerged from the research literature (1) the matching process (i.e., how matches are made and facilitated by practitioners such as incorporating participant input on matches): and (2) individual characteristics (i.e., individual differences that may serve as matching criteria such as experiential, surface-level, and deep-level characteristics). This qualitative review resulted in three practical recommendations to practitioners interested in matching mentors and protégés using evidence-based methods: (1) match based on deep-level similarities, (2) consider developmental-needs of protégés during matching, and (3) seek mentors' and protégés’ input before finalizing matches. Research limitations/implications Limitations of the research reviewed are highlighted: measures of perceived similarity, relative effectiveness of matching-related factors, limited research investigating the role of dissimilarity on mentoring outcomes, and linear relationship assumptions between matching-related factors and mentoring outcomes. Practical implications The authors’ recommendations suggested greater use of valid psychometric assessments to facilitate matching based on actual assessed data rather than program administrators' personal knowledge of mentors and protégés. Originality/value The literature on mentor–protégé matching is missing practical guidance on how to apply the research. This highlights a need for a qualitative review of the literature to identify what matching processes and criteria are most effective, providing a “one-stop-shop” for practitioners seeking advice on how to construct effective mentor–protégé matches in formal mentorship programs.
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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 | 0.115 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".