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The Mentee Perspective: Evaluating Mentorship of Medical Students in the Field of Orthopaedic Surgery

2023· article· en· W4388488576 on OpenAlexaff
Sudarsan Murali, Andrew B. Harris, Morgan Snow, Dawn M. LaPorte, Amiethab A. Aiyer

Bibliographic record

VenueJAAOS Global Research and Reviews · 2023
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsObject Research Systems (Canada)Weyerhauser (Canada)
Fundersnot available
KeywordsMentorshipMedical educationMedicinePerspective (graphical)Orthopedic surgeryPsychologySurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Mentorship is an invaluable facet of medical education. The purpose of this study was to analyze medical student perspectives of mentorship they received and the influence this has on their participation in the field of orthopaedic surgery. METHODS: We conducted a cross-sectional study of medical students interested in pursuing orthopaedic surgery through an 18-question survey distributed through social media and e-mail. RESULTS: Two hundred fifteen students completed this survey, with over 50% of students reporting that they have a mentor in orthopaedic surgery while 34% were actively seeking one. Most students found mentors through research opportunities (25%) and cold e-mails (20%). Common hurdles to mentorship were access (38%) and finding common time (30%). Peer mentorship had a higher mean satisfaction score in all domains, except facilitating matching, and there was a significant difference between groups (e.g., peer mentor versus program director; P < 0.001). Sex, race, and degree type were not significantly related to students' access to or their evaluation of mentors (P > 0.05 for all). CONCLUSION: Overall, this study demonstrates that medical students across the nation rely on mentorship to guide them on their path to becoming an orthopaedic surgeon.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.054
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0540.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.276
GPT teacher head0.594
Teacher spread0.318 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
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

Citations6
Published2023
Admission routes1
Has abstractyes

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