Why do surgeons schedule their own surgeries?
Bibliographic record
Abstract
Abstract Surgery is a knowledge intensive, high‐risk professional service. Most hospitals give surgeons considerable autonomy in deciding which patients to operate on and when. In theory, this allows surgeons the operational flexibility to prioritize surgeries based on intimate knowledge of their patient's clinical needs. At odds with this strategy is the operations management literature, which favors the standardization and centralization of scheduling focused on achieving the efficient use of all resources, such as operating room capacity. Unfortunately, a little is known as to how surgeons customize their schedules and why they value such control. To this end, we conduct an exploratory qualitative study of the scheduling behavior of surgeons at a large Canadian teaching hospital. We identify significant differences between surgeons as to their priorities when scheduling. Two constructs are formative in surgeon decision‐making: the timeliness of treatment for their patients and idiosyncratic personal priorities. Our work has implications for achieving surgeon support for initiatives to standardize and centralize routines for patient scheduling. Accordingly, we formulate propositions that address the conditions under which such efforts will achieve the desired balance between flexibility and efficiency.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".