Training and Performance Differences in US Internal Medicine Residents Trained in Community- and University-Based Programs – a Systematic Review
Bibliographic record
Abstract
Introduction:Residents’ learning and performance depends on program structures, clinical setting and faculty mentors; however,performance differences between and community based vs. university based residents have not been exploredsystematically.Objectives:To systematically review the performance differences between internal medicine residents trained in community-basedprograms [CBPs] versus university-based programs [UBPs] in the US.Methods:Eligible studies were identified in Medline and Embase databases from 1990- June 2018. Eligible studies comparedlearning and performance differences between UBP and CBP internal medicine residency programs aligned withACGME recommendations.Results:Out of 4916 titles, 14 cross-sectional studies were included in the analysis. Diverse reporting among the includedstudies precluded meta-analysis. Significant differences were found in specific practice areas, such as knowledge aboutHIV, nutrition training, and program accreditation cycle. Residents in UBPs participated more often in hypothesisdriven research and had higher publication rates than residents in CBPs. Residents trained in CBPs experienced moreburnt out than those in UBPs and had higher prevalence of residents with problematic behaviors and deficiencies.Nonsignificant differences were found among residents regarding ABIM pass rate, medical procedures, and publichealth training.Conclusion:Our review reports inconsistent trends in residents’ learning and performances following RRC- IM and ACGMErecommendations. Significant differences were noted in areas that required more practice and system based learning,non-procedural skills and patient care. Future studies with larger sample sizes and adjusted analyses are needed toevaluate the difference between residents’ performance and learning in UBPs versus CBPs.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".