Prevalence and Causes of Attrition Among Surgical Residents
Bibliographic record
Abstract
Importance: Attrition of residents from general surgery training programs is relatively high; however, there are wide discrepancies in the prevalence and causes of attrition reported among surgical residents in previous studies. Objective: To summarize the estimate of attrition prevalence among general surgery residents. Data Sources: We searched the Medline, EMBASE, Cochrane, PsycINFO, and ERIC databases (January 1, 1946, to October 22, 2015) for studies reporting on the prevalence and causes of attrition in surgical residents, as well as the characteristics and destinations of residents who left general surgery training programs. Database searches were conducted on October 22, 2015. Study Selection: Eligibility criteria included all studies reporting on the primary (attrition prevalence) or secondary (causes of attrition and characteristics and destination of residents who leave residency programs) outcomes in peer-reviewed journals. Commentaries, reviews, and studies reporting on preliminary surgery programs were excluded. Of the 41 full-text articles collected from the title/abstract screening, 22 studies (53.7%) met the selection criteria. Data Extraction and Synthesis: Two reviewers independently collected and summarized the data. We calculated pooled estimates using random effects meta-analyses where appropriate. Main Outcome and Measure: Attrition prevalence of general surgery residents. Results: Overall, we included 22 studies that reported on residents (n = 19 821) from general surgery programs. The pooled estimate for the overall attrition prevalence among general surgery residents was 18% (95% CI, 14%-21%), with significant between-study variation (I2 = 96.8%; P < .001). Attrition was significantly higher among female compared with male (25% vs 15%, respectively; P = .008) general surgery residents, and most residents left after their first postgraduate year (48%; 95% CI, 39%-57%). Departing residents often relocated to another general surgery program (20%; 95% CI, 15%-24%) or switched to anesthesia (13%; 95% CI, 11%-16%) and other specialties. The most common reported causes of attrition were uncontrollable lifestyle (range, 12%-87.5%) and transferring to another specialty (range, 19%-38.9%). Conclusions and Relevance: General surgery programs have relatively high attrition, with female residents more likely to leave their training programs than male residents. Residents most often relocate or switch to another specialty after the first postgraduate year owing to lifestyle-related issues.
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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 | 0.046 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".