Residency Training in Plastic Surgery: A Survey of Educational Goals
Bibliographic record
Abstract
With the advent of integrative plastic surgical training programs, requirements for earlier specialization decisions, and an increasing subspecialization within the practice of plastic surgery, the educational goals of residency training may have changed. The duration and extent of training required are also currently being questioned. This study was performed to better understand the necessary roles of plastic surgery residencies and to determine how these demands might optimally be met. Of 151 practicing plastic surgeons in the Ontario, Canada, region, 81 (53.6 percent) responded to a survey. General agreement was that 2 years was an optimal length of time for core surgical training, which should then be followed by at least 3 years of plastic surgical training. Opinions on the ideal length of time training in specific medical and surgical disciplines are discussed. Overall, respondents thought that two thirds of training should occur in tertiary care centers, with the remaining time spent at smaller community centers and private clinics. Nearly half of respondents thought that research training should be a mandatory part of the residency, although the amount of protected time for this activity varied substantially. Most thought that unrestricted elective time should also be available. Academic plastic surgeons rated the importance of research training (p < 0.01), critical appraisal skills (p < 0.05), and teaching skills (p < 0.05) as significantly more important than did their nonacademic colleagues. The authors present results from the Ontario region and a template for determining optimal characteristics for training programs. Further investigation may be of timely importance during a foreseeable future transition from traditional to integrative plastic surgery residency training.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".