Radiology Resident' Satisfaction With Their Training and Education in the United States: Effect of Program Directors, Teaching Faculty, and Other Factors on Program Success
Bibliographic record
Abstract
OBJECTIVE: Radiology residency education must evolve to meet the growing demands of radiology training. Resident opinions are a major resource to identify needs. However, few published data are available on a national level investigating the radiology resident perspective on factors that influence the resident experience. Our study investigates factors that affect residents' satisfaction with their residency experience and education. MATERIALS AND METHODS: A 67-item survey was sent to all radiology residency program directors and coordinators in the United States to be distributed at their discretion. Questions were multiple choice, free-text answer, or 5-point Likert scale. Statistical significance (p < 0.05) was determined using chi-square test, t test, and logistic regression analysis, respectively. RESULTS: Two hundred seventeen radiology residents responded to the survey (range, 212-217 responses per question). Overall, 77.8% (168/216) of residents were satisfied with their residency programs. Subcategories that showed a statistically significant correlation with overall satisfaction, in decreasing strength according to the odds ratio (OR), include the program director or administrative office (OR, 72.2; 95% CI, 27.4-221.9), the daily workstation experience (OR, 30.5; 95% CI, 12.8-80.9), the faculty (OR, 19.5; 95% CI, 8.9-45.4), educational conferences (OR, 7.9; 95% CI, 3.9-16.4), work hours (OR, 6.4; 95% CI, 3.2-13.2), teaching opportunities (OR, 6.5; 95% CI, 3.1-13.8), research opportunities (OR, 5.1; 95% CI, 2.6-10.6), personal study (OR, 2.1; 95% CI, 1.1-4.1), and compensation (OR, 1.9; 95% CI, 1.0-3.7). CONCLUSION: Our study provides incremental data to the existing literature that offers insight into factors that contribute to a successful radiology residency program.
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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) | 0.003 | 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".