Objective Predictors of Grit, Self-Control, and Conscientiousness in Orthopaedic Surgery Residency Applicants
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to identify objective predictors of grit, self-control, and conscientiousness in orthopaedic surgery residency applicants. METHODS: The following attributes were assessed in 455 applicants: grit, self-control, conscientiousness, consistency of interest, perseverance of effort, and ambition. These measures were correlated with standard, objective demographics obtained during the application process. RESULTS: Alpha Omega Alpha status, additional degrees, and number of publications did not predict any of the studied attributes. Grit increased with age (P < 0.001) but decreased with increasing board scores (P = 0.004). Former collegiate athletes demonstrated greater grit (P < 0.001), consistency of interest (P = 0.007), perseverance (P = 0.006), and self-control (P = 0.019). Female applicants demonstrated more grit (P = 0.044), consistency of interest (P = 0.003), and conscientiousness (P = 0.029) than males. Applicants with military experience had increased ambition (P = 0.033) and conscientiousness (P = 0.001). CONCLUSION: Overall, orthopaedics applicants possess increased grit compared with the general public, and a number of objective variables reliably predicted the studied attributes. LEVEL OF EVIDENCE: Level III, Cross-sectional study.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".