179:oral Priority-setting for effective pandemic preparedness: a case study of priority setting for COVID-19 in the Western Pacific Region
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
Method 2316 members of a representative panel of doctors practicing in Norway received a questionnaire in December 2020. Data were analysed by descriptive statistics and regression analyses. Results 1617 of 2316 (70%) responded. A majority reported familiarity with the official priority criteria, but not with the particular legislation on priority setting (the Priority Regulation/Prioriteringsforskriften), or the Directorate of Health's Guidelines for priority setting during the pandemic. 60-74% did not use guidelines for priority setting. 60,5% experienced that some of their patients got lower priority for treatment. Of these, 47% considered this medically indefensible to some/ a large extent. We saw a significant difference between GPs, hospital doctors and private specialists in considering the lower priority indefensible: 42,6% (hospital doctors), and 57,8% (GPs). Regression analysis showed that increased age involved fewer claims of lower priority, controlling for age and workplace, while working in primary care increased the probability of considering the priorities medically indefensible, controlling for age and gender. Discussion If priority setting in clinical practice is to proceed in accordance with priority setting principles and guidelines, doctors' familiarity with them must improve. Apparently, the clinical priority setting in response to the pandemic was considered medically indefensible by many doctors. One interpretation is that doctors have judged that the rationing of care went too far; another is that the society, including politicians, patients, and doctors, find it hard to accept rationing of care for particular patient groups.
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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 | 0.037 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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