Optimizing a mentorship program from the perspective of academic medicine leadership – a qualitative study
Bibliographic record
Abstract
BACKGROUND: Effective mentorship is an important contributor to academic success. Given the critical role of leadership in fostering mentorship, this study sought to explore the perspectives of departmental leadership regarding 1) current departmental mentorship processes; and 2) crucial components of a mentorship program that would enhance the effectiveness of mentorship. METHODS: Department Division Directors (DDDs), Vice-Chairs, and Mentorship Facilitators from the Department of Medicine at the University of Toronto Temerty Faculty of Medicine were interviewed between April and December 2021 using a semi-structured guide. Interviews were audio-recorded and transcribed verbatim, then coded. Analysis occurred in 2 steps: 1) codes were organized to identify emergent themes; then 2) the Social Ecological Model (SEM) was applied to interpret the findings. RESULTS: Nineteen interviews (14 DDDs, 3 Vice-Chairs, and 2 Mentorship Facilitator) were completed. Analysis revealed three themes: (1) a culture of mentorship permeated the department as evidenced by rigorous mentorship processes, divisional mentorship innovations, and faculty that were keen to mentor; (2) barriers to the establishment of effective mentoring relationships existed at 3 levels: departmental, interpersonal (mentee-mentor relationships), and mentee; and (3) strengthening the culture of mentorship could entail scaling up pre-existing mentorship processes and promoting faculty engagement. Application of SEM highlighted critical program features and determined that two components of interventions (creating tools to measure mentorship outcomes and systems for mentor recognition) were potential enablers of success. CONCLUSIONS: Establishing 'mentorship outcome measures' can incentivize and maintain relationships. By tangibly delineating departmental expectations for mentorship and creating systems that recognize mentors, these measures can contribute to a culture of mentorship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".