Recognizing diversity in public preferences: The use of preference sub‐groups in cost‐effectiveness analysis
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
Public preferences are typically incorporated into cost-effectiveness analyses (CEA) on the basis of the average health state utilities of a sample of individuals drawn from the general public. The cost-effectiveness of a programme is then assessed on an 'all-or-nothing' basis: the programme is declared either cost-effective or not for all patients in clinically homogeneous sub-groups. However, this approach fails to recognize variability between individuals in their preferences. In this paper, we consider how diversity in the preferences of individuals can be handled within CEA when the public's preferences are considered appropriate for defining benefit, with the objective of increasing the efficiency of health care delivery. The concept of preference sub-group analysis is described and some of its implications are assessed. These include the methods that could be used to identify sub-groups from amongst public raters, the appropriate approach to eliciting preferences and the possible implications of preference sub-group analysis for clinical decision making.
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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 | 0.020 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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