Why do surgeons schedule their own surgeries?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it