Threshold concepts in group-based mentoring and implications for faculty development: A qualitative analysis
Bibliographic record
Abstract
The literature on faculty development programs for mentors is scarce. This study examines mentors’ experiences and challenges, with the aim of identifying threshold concepts in mentoring. It also discusses the implications for the faculty development of mentors. Semi-structured interviews solicited personal narratives and reflections on mentors’ lived experiences. Data analysis was guided by the threshold concepts framework allowing for the identification of significant and transformative shifts in perspectives. We interviewed 22 mentors from two Norwegian and one Canadian medical school with group-based mentoring programs. The mentoring experience involved four significant threshold concepts: focusing on students’ needs; the importance of creating a trusting learning space; seeing oneself through the eyes of students; and aligning mentor and physician identities. Taking on a mentor role can provoke personal and professional dilemmas while also sparking growth. The trajectories of developing as a mentor and as a professional physician may be seen to mutually validate, mirror and reinforce each other. Faculty development programs designed specifically for mentors should aim to stimulate reflection on previous learning experiences and strive for a successful alignment of the distinct pedagogical and clinical content knowledge required to fulfill various professional roles.
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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 | 0.035 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".