Patients’ views on priority setting in neurosurgery: A qualitative study
Bibliographic record
Abstract
Object. Accountability for Reasonableness is an ethical framework which has been implemented in various health care systems to improve and evaluate the fairness of priority setting. This framework is grounded on four mandatory conditions: relevance, publicity, appeals, and enforcement. There have been few studies which have evaluated the patient stakeholders’ acceptance of this framework; certainly no studies have been done on patients’ views on the prioritization system for allocating patients for operating time in a system with pressure on the resource of inpatient beds. The aim of this study is to examine neurosurgical patients’ views on the prioritization of patients for operating theater (OT) time on a daily basis at a tertiary and quaternary referral neurosurgery center. Methods. Semi-structured face-to-face interviews were conducted with thirty-seven patients, recruited from the neurosurgery clinic at Toronto Western Hospital. Family members and friends who accompanied the patient to their clinic visit were encouraged to contribute to the discussion. Interviews were audio recorded, transcribed verbatim, and subjected to thematic analysis using open and axial coding. Results. Overall, patients are supportive of the concept of a priority-setting system based on fairness, but felt that a few changes would help to improve the fairness of the current system. These changes include lowering the level of priority given to volume-funded cases and providing scheduled surgeries that were previously canceled a higher level of prioritization. Good communication, early notification, and rescheduling canceled surgeries as soon as possible were important factors that directly reflected the patients’ confidence level in their doctor, the hospital, and the health care system. Conclusion. This study is the first clinical qualitative study of patients’ perspective on a prioritization system used for allocating neurosurgical patients for OT time on a daily basis in a socialized not-for-profit health care system with fixed resources.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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 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".