The liminal landscape of mentoring—Stories of physicians becoming mentors
Bibliographic record
Abstract
INTRODUCTION: This study explores narratives of physicians negotiating liminality while becoming and being mentors for medical students. Liminality is the unstable phase of a learning trajectory in which one leaves behind one understanding but has yet to reach a new insight or position. METHODS: In this study, we analysed semi-structural interviews of 22 physician mentors from group-based mentoring programmes at two Norwegian and one Canadian medical school. In a dialogical narrative analysis, we applied liminality as a sensitising lens, focusing on informants' stories of becoming a mentor. RESULTS: Liminality is an unavoidable aspect of developing as a mentor. Which strategies mentors resort to when facing liminality are influenced by their narrative coherence. Some mentors thrive in liminality, enjoying the possibility of learning and developing as mentors. Others deem mentoring and the medical humanities peripheral to medicine and thus struggle with integrating mentor and physician identities. They may contradict themselves as they shift between their multiple identities, resulting in rejection of the learning potentials that liminality affords. CONCLUSION: Mentors with integrated physician and mentor identities can embrace liminality and develop as mentors. Those mentors with contradicting dialogues between their identities may avoid liminality if it challenges their understanding of who they are and make them experience discomfort, confusion and insufficiency while becoming a mentor. Support of the mentoring role from the clinical culture may help these physicians develop internal dialogues that reconcile their clinician and mentor identities.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".