The (non) use of prioritisation protocols by surgeons
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
Priority setting and rationing is a dominant feature of contemporary health policy. In New Zealand, clinical priority assessment criteria (CPAC) tools have been developed to make access to elective surgery more equitable and efficient. Research was undertaken to identify how surgeons used these tools in the consultation. Forty-seven consultations with 15 different surgeons have to date been video- and audio-recorded. There were no instances where CPAC tools were explicitly used in the consultation. Drawing on the methodology of conversation analysis and the concept of news delivery as developed by Maynard, this paper argues that the delivery of diagnoses and treatment plans can usefully be seen in part as the delivery of bad or good news. Using three case studies to illustrate the argument, it is suggested that the interactional work required in the delivery of such news challenges the ability of clinicians to use protocols such as CPAC. The analysis sheds light on important consultation processes that need to be more carefully considered when designing interventions to influence clinician behaviour. In order to influence the behaviour of clinicians to achieve policy goals, greater attention needs to be paid to the interactional demands of the consultation process.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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