Investigating divergent thinking and creative ability in surgeons (IDEAS): a survey protocol
Bibliographic record
Abstract
INTRODUCTION: A strong pipeline of creative ideas and individuals is critical if we are to tackle the complex healthcare challenges we will face in the 21st century. The field of creativity is severely underinvestigated in the context of surgery, and it is of interest to explore the level and nature of creativity in surgeons, across various specialties and backgrounds. Identifying the areas of surgery with strong and weak levels of creativity, as well as the predictors of high creativity among surgeons, may aid in the selection and training of future surgeons. METHODS AND ANALYSIS: A convenience sample of surgeons from the Department of Surgery and McMaster University will be used for the recruitment of participants. The Abbreviated Torrance Test for Adults, a three-part test of divergent thinking ability, will be administered to measure the level and nature of creativity among surgeons. Descriptive analyses and multiple linear regression models are planned to synthesise the results of the survey and identify predictors of divergent thinking ability among surgeons. ETHICS AND DISSEMINATION: Ethics approval from the Hamilton Integrated Research Ethics Board was obtained. No harm is expected due to participation in this study. The results of this survey will be published in a peer-reviewed journal and disseminated through conferences and presentations at the regional, national and international levels.
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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 | 0.084 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".