Mapping a shared care model in complex gastrointestinal surgery: A qualitative study of queues and stakeholders within a Canadian general surgery practice
Bibliographic record
Abstract
Shared Care Models (SCMs), in which a team of clinicians share in patient care and resource utilization, represent an opportunity for surgeon-level system change. We aimed to identify the queues and stakeholders within a complex gastrointestinal surgical care pathway to demonstrate the implications of a SCM on system efficiency. A multidisciplinary group of surgeons and care navigators working in SCMs were asked to develop a patient encounter map through consensus to illustrate relevant queues and stakeholders within a SCM. Fifteen surgeon-related queues were identified, each representing a point of potential delay to care in the patient's journey that could be addressed by shared care. A final patient encounter map was created, and advantages and challenges of SCMs were also described from multidisciplinary group discussions. The numerous queues identified in this map ultimately reflected opportunities for more efficient care navigation under a SCM through increased surgeon availability and shared resource utilization.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".