Grit and Thoracic Surgery Interest Among Medical Students
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".