Use of, and attitudes to, clinical priority assessment criteria in elective surgery in New Zealand
Bibliographic record
Abstract
OBJECTIVES: To describe the ways patients access elective surgery in New Zealand, and to understand the use of, and attitudes to, clinical priority assessment criteria (CPAC) in determining access to publicly funded elective surgery. METHODS: A qualitative study in selected New Zealand localities. A purposive sample of general practitioners, surgeons and administrators in publicly funded hospitals were interviewed. Data were analysed by a process of thematic analysis. RESULTS: Sixty-five interviews were completed. General practitioners had a key role in determining which patients were seen in the public sector and, by utilising strategies to actively advocate for patients, influenced both waiting times for first assessment by surgeons and for surgery. CPAC had been developed as decision support guides with the intention that they would provide transparency and equity in determining access. However, there was variation in the way CPAC were being used both in score construction and in the influence of the score on access to surgery. The management of the hospital system also limited the extent to which CPAC could be used to prioritise patients for surgery. CONCLUSIONS: Variability in the use of CPAC tools meant that at the time of the study they did not provide a transparent and equitable method of determining access to surgery. This highlights the difficulties in developing and implementing CPAC and suggests that further development is difficult in the absence of evidence to identify patients who will benefit the most from surgery.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".