A Global Delphi Consensus Study on Defining and Measuring Quality in Surgical Training
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that patient outcomes can be associated with the quality of surgical training. To raise the standards of surgical training, a tool to measure training quality is needed. The objective of this study was to define the elements of high-quality surgical training and methods to measure them. STUDY DESIGN: Modified Delphi methodology was used to achieve international expert consensus. Seventy statements about indicators and measures of training quality were developed based on themes from semi-structured interviews of surgeons. Eighty-three experts in surgical education from 13 countries were invited to complete an online survey ranking each statement on a 5-point Likert scale. Consensus was predefined as Cronbach's α ≥0.80. Once consensus was achieved, statements ranked ≥4 by ≥80% of experts were used as themes to develop the Surgical Training Quality Assessment Tool (S-QAT). RESULTS: Fifty-three (64%) experts from 11 countries responded. Consensus was achieved after 2 rounds of voting (Cronbach's α = 0.930). Thirty-five statements were selected as themes for the Surgical Training Quality Assessment Tool. Statements defining training quality covered the following subjects: relationship between the trainer and trainee, operative exposure, supervision, feedback, structure and organization, and structured teaching programs. Consensus statements on measuring training quality included trainee feedback, trainer feedback, timetable structure, and trainee improvement. There was agreement that measuring training quality would have a positive effect on training. CONCLUSIONS: International expert consensus was achieved on defining and measuring high-quality surgical training. This has been translated into the (S-QAT) to evaluate surgical training programs. Competition created by comparing training quality might raise the standards of surgical education.
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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 | 0.366 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".