Factors Influencing Resident Satisfaction and Fellowship Selection in Orthopaedic Training Programs
Bibliographic record
Abstract
BACKGROUND: There is limited literature available about educational satisfaction and fellowship selection among orthopaedic surgery residents. The purpose of this study was to identify factors that influence resident subspecialty career choice, fellowship selection, and satisfaction with orthopaedic training programs. METHODS: A self-report survey was electronically administered to orthopaedic surgery residents at 44 academic centers in the United States and Canada. Basic demographic information and level of satisfaction with a number of factors (surgical independence, mentorship opportunities, etc.) were evaluated using a 5-point Likert scale ranging from "excellent" to "poor." Summary statistics and group differences for discrete variables were compared with use of a chi-square test. RESULTS: Of the 283 respondents, 77% rated residency program satisfaction as "very good" or "excellent," and 93% said they would choose the same training program again. Decreased surgical independence (p < 0.01), poor faculty reputation (p < 0.01), reduced volume and variety of cases (p < 0.01), inadequate mentorship (p < 0.01), and reduced educational opportunities (p < 0.01) were associated with low satisfaction. Surgical variety and job opportunities were the top 2 factors contributing to subspecialty choice. Sports medicine and joints were the most popular career choices; case volume, surgical variety, and program reputation were the top factors contributing to fellowship program selection. CONCLUSIONS: In order to achieve resident satisfaction, orthopaedic training programs should strive to improve resident surgical independence, surgical case variety, mentorship programs, faculty reputation, and educational opportunities. Important factors for fellowship program selection include case volume, surgical variety, and overall program reputation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".