Who Makes It to the End?
Bibliographic record
Abstract
OBJECTIVE: We present 8-year follow-up data from the intern class of 2007 to 2008 using a novel, nonparametric predictive model to identify those residents who are at greatest risk of not completing their training. BACKGROUND: Nearly 1 in every 4 categorical general surgery residents does not complete training. There has been no study at a national level to identify individual resident and programmatic factors that can be used to accurately anticipate which residents are most at risk of attrition out. METHODS: A cross-sectional survey of categorical general surgery interns was conducted between June and August 2007. Intern data including demographics, attendance at US or Canadian medical school, proximity of family members, and presence of family members in medicine were de-identified and linked with American Board of Surgery data to determine residency completion and program characteristics. A Classification and Regression Tree analysis was performed to identify groups at greatest risk for non-completion. RESULTS: Of 1048 interns, 870 completed the initial survey (response rate 83%), 836 of which had linkage data (96%). Also, 672 residents had evidence of completion of residency (noncompletion rate 20%). On Classification and Regression Tree analysis, sex was the independent factor most strongly associated with attrition. The lowest noncompletion rate for men was among interns at small community programs who were White, non-Hispanic, and married (6%). The lowest noncompletion rate for women was among interns training at smaller academic programs (11%). CONCLUSIONS: This is the first longitudinal cohort study to identify factors at the start of training that put residents at risk for not completing training. Data from this study offer a method to identify interns at higher risk for attrition at the start of training, and next steps would be to create and test interventions in a directed fashion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".