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Record W4389340433 · doi:10.1111/tct.13697

The new mentee: Exploring Gen Z women medical students' mentorship needs and experiences

2023· article· en· W4389340433 on OpenAlexaffabout
Calandra Li, Paula Veinot, Maria Mylopoulos, Fok‐Han Leung, Marcus Law

Bibliographic record

VenueThe Clinical Teacher · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsMentorshipFeelingMedical educationWorkforcePsychologyPopulationMedicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.005
Scholarly communication0.0060.002
Open science0.0020.009
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.225
GPT teacher head0.449
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainIncentives
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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