A framework for role allocation in education, research and leadership services in Canadian academic divisions of general surgery: a modified Delphi consensus
Bibliographic record
Abstract
BACKGROUND: Moving toward a funding standard similar to that for clinical services for roles essential to the functioning of education, research and leadership services within divisions of general surgery is necessary to strengthen divisional resilience. We aimed to identify roles and underlying tasks in these services central to sustainable functioning of Canadian academic divisions of general surgery. METHODS: = 12]) to achieve national consensus from an expert panel of all 17 heads of academic divisions of general surgery in Canada on the roles and accompanying tasks essential to education, research and leadership services within an academic division of general surgery. We used 70% agreement to determine consensus. RESULTS: The expert panel agreed that a framework for role allocation in education, research and leadership services was relevant and necessary. Consensus was reached for 7 roles within the educational service, 3 roles within the research service and 5 roles within the leadership service. CONCLUSION: Our framework represents a national consensus that defines role standards for education, research and leadership services in Canadian academic divisions of general surgery. The framework can help divisions build resiliency, and enable sustained and deliberate advances in these services.
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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 | 0.302 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".