The new mentee: Exploring Gen Z women medical students' mentorship needs and experiences
Bibliographic record
Abstract
PURPOSE: The incoming Canadian cohort of medical students is comprised mainly of individuals from Generation Z (Gen Z; born between 1997 and 2012), with greater than 50% of applicants identifying as female. A gap remains in our understanding of Gen Z women learners in their challenges in navigating medical education, their expectations for their medical careers and the influences that have impacted their worldview. This study explored the needs, values, and experiences of Gen Z women medical students and the impact of these factors on mentorship expectations among this population that will soon be entering the workforce. METHODS: Upon receiving ethics approval from the University of Toronto Research Ethics Board, semi-structured interviews were conducted (February-May 2021) with 15 Gen Z women students from 14 English-speaking Canadian medical schools who had given written consent to participate. An iterative constant comparative team approach was utilised in which the interview guide and sampling were adjusted as the data evolved. Transcripts were line by line coded into categories, then grouped into themes using descriptive analysis. RESULTS: These socially aware learners described how society had afforded them greater opportunities for expression, which gave them a sense of feeling advantaged over older generations. However, participants paradoxically expressed feelings of powerlessness and commented on tensions they experienced when interacting with older generation physician mentors, especially during conversations on social justice issues. They also highlighted instances of biased mentorship specific to their gender. Participants emphasised a desire for inclusive mentorship that considered the mentee's identity and intersectionality. CONCLUSIONS: The growing number of women learners in Canadian medical schools necessitates a re-evaluation of mentorship delivery. Mentors must adapt by integrating Gen Z ideals to overcome mentorship challenges.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".