Choosing vs. allocating: discrete choice experiments and constant‐sum paired comparisons for the elicitation of societal preferences
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
BACKGROUND: There is growing evidence of a reluctance to allocate health care solely on the basis of maximizing quality-adjusted life years (QALYs). Stated preference methods can be used to elicit preferences for efficiency vs. equity in the allocation of health-care resources. OBJECTIVE: To compare discrete choice experiment (DCE) and constant-sum paired comparison (CSPC) methods for eliciting societal preferences. METHODS: Over a series of choice pairs, DCE respondents allocated a fixed budget to one preferred group and CSPC respondents allocated budget percentages between the groups. Questionnaires were compared in terms of completion rates, preference consistency, dominant preferences and derived attribute importance. RESULTS: There was no significant difference in the proportions that rated the questionnaires somewhat or extremely difficult, but a significantly greater proportion completed the DCE compared to the CSPC. Preference consistency was also higher in the DCE. The incidence of dominant preferences, including for aggregate QALYs, was low and not significantly different between questionnaires. Similarly, no CSCP respondents equalized budgets or outcomes in every task. Final health state was the most important attribute in both questionnaires, but the rankings diverged for the other attributes. Notably, the total patients' treated attribute was important in the CSPC but insignificant in the DCE, perhaps reflecting a 'prominence effect'. CONCLUSIONS: Despite lower completion rates and preference consistency, CSPC may offer advantages over DCE in eliciting preferences over the distribution of resources and/or outcomes as well as attribute levels, avoiding extreme 'all-or-nothing' distributions and possibly aligning respondent attention more closely with a societal perspective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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