Transition to Surgical Residency
Bibliographic record
Abstract
PURPOSE: To evaluate interns' perceived preparedness for defined surgical residency responsibilities and to determine whether fourth-year medical school (M4) preparatory courses ("bootcamps") facilitate transition to internship. METHOD: The authors conducted a multi-institutional, mixed-methods study (June 2009) evaluating interns from 11 U.S. and Canadian surgery residency programs. Interns completed structured surveys and answered open-ended reflective questions about their preparedness for their surgery internship. Analyses include t tests comparing ratings of interns who had and had not participated in formal internship preparation programs. The authors calculated Cohen d for effect size and used grounded theory to identify themes in the interns' reflections. RESULTS: Of 221 eligible interns, 158 (71.5%) participated. Interns self-reported only moderate preparation for most defined care responsibilities in the medical knowledge and patient care domains but, overall, felt well prepared in the professionalism, interpersonal communication, practice-based learning, and systems-based practice domains. Interns who participated in M4 preparatory curricula had higher self-assessed ratings of surgical technical skills, professionalism, interpersonal communication skills, and overall preparation, at statistically significant levels (P < .05) with medium effect sizes. Themes identified in interns' characterizations of their greatest internship challenges included anxiety or lack of preparation related to performance of technical skills or procedures, managing simultaneous demands, being first responders for critically ill patients, clinical management of predictable postoperative conditions, and difficult communications. CONCLUSIONS: Entering surgical residency, interns report not feeling prepared to fulfill common clinical and professional responsibilities. As M4 curricula may enhance preparation, programs facilitating transition to residency should be developed and evaluated.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".