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Record W4380302188 · doi:10.36834/cmej.74694

Attitudes of Canadian medical students towards surgical training and perceived barriers to surgical careers: a multicentre survey

2023· article· en· W4380302188 on OpenAlexaffvenueabout
Steffane McLennan, Kieran Purich, Kevin Verhoeff, Brett Mador

Bibliographic record

VenueCanadian Medical Education Journal · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWorryMedicinePerceptionMedical educationFamily medicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Background: Medical student interest in surgical specialties continues to decline. This study aims to characterize attitudes of Canadian medical students towards surgical training and perceived barriers to surgical careers. Methods: An anonymous survey was custom designed and distributed to medical students at the University of Alberta and University of Calgary. Survey questions characterized student interest in surgical specialties, barriers to pursuing surgery, and influence of surgical education opportunities on career interest. Results: Survey engagement was 26.7% in 2015 and 24.2% in 2021. General surgery had the highest rate of interest in both survey years (2015: 38.3%, 2021: 39.2%). The most frequently reported barrier was worry about the stress that surgical careers can put on personal relationships (2015: 70.9%, 2021: 73.8%, p = 0.50). Female respondents were significantly more likely to cite gender discrimination as a deterrent to surgical careers (F: 52.0%, M: 5.8%, p < 0.001). Conclusions: Despite substantial interest, perception of work-life imbalance was the primary reported barrier to surgical careers. Further, female medical students’ awareness of gender discrimination in surgery highlights the need for continued efforts to promote gender inclusivity within surgical disciplines to support early career women interested in surgery.

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)0.010
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0410.000

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 head0.046
GPT teacher head0.360
Teacher spread0.315 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations8
Published2023
Admission routes3
Has abstractyes

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