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Record W4399096699 · doi:10.1016/j.jsurg.2024.05.008

Grit and Thoracic Surgery Interest Among Medical Students

2024· article· en· W4399096699 on OpenAlexaffabout
Bright Huo, Todd Dow, Alison Wallace, Daniel French

Bibliographic record

VenueJournal of surgical education · 2024
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsMcMaster UniversityDalhousie UniversityHamilton General Hospital
Fundersnot available
KeywordsGritMedicinePsychologyGeneral surgerySurgerySocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: This study evaluated the relationship between medical student Grit and thoracic surgery career interest. DESIGN: An online questionnaire was designed to measure self-reported ratings of Grit among medical student using the Short-Grit scale, as well as thoracic surgery career interest. SETTING: Faculty of Medicine, Dalhousie University, Halifax, NS, Canada. PARTICIPANTS: From 2019 to 2021, 192/367 (52.3%) participants in their first or second year of medical school. The cohort was comprised of 109 (56.8%) females while 115 (59.9%) were <24 years of age. RESULTS: Mean Grit was high (M = 4.159 +/- 0.450) among medical students. There were 80 (41.2%) students interested in thoracic surgery. There was a significant difference in Grit between students with a career interest in thoracic surgery (4.256 +/- 0.442) and those uninterested in thoracic surgery (4.089 +/- 0.444); t(190) = 2.572, p = 0.011; Cohen's D = 0.442. Career interest in thoracic surgery was not influenced by career factor interest. CONCLUSIONS: Grittier students have a career interest in thoracic surgery. Recruitment teams in thoracic surgery residency programs with high rates of burnout and poor psychological wellbeing among trainees may take interest in these findings.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.412
Teacher spread0.369 · 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 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

Citations2
Published2024
Admission routes2
Has abstractyes

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