Validity, Reliability, and Framing Effects in Equity-Efficiency Trade-Off Studies: A Systematic Review
Bibliographic record
Abstract
Abstract Objectives Health system resources are limited, and decision makers often need to make trade-offs between equity and efficiency. Such trade-offs are guided by public values that are elicited using choice experiments. There is no standard approach to elicit equity-efficiency trade-offs. Previous studies have found significant variability in public values, raising concerns about the robustness of trade-off experiments. The objective of this systematic review was to determine whether and how equity-efficiency trade-off studies consider validity, reliability and framing effects. Methods We searched Medline, EMBASE, and Web of Science in January 2024 for health-related equity-efficiency trade-off studies. Two reviewers independently screened titles and abstracts, followed by full-text review, data extraction, and quality assessment. Results 115 equity-efficiency trade-off studies were identified, of which 29 studies (25.2%) investigated validity, reliability, and/or framing effects. 14 (12.2%) studies assessed validity, 9 (7.8%) studies assessed reliability, and 10 (8.7%) assessed framing or cognitive effects. Validity was most frequently assessed by comparing results to hypothesized expectations, while reliability was commonly assessed by providing a repeated test or questionnaire. Framing and cognitive effects were assessed by varying question order or changing the wording or framing of the scenario. 19 of 23 studies reported high or acceptable validity or reliability, and 5 of 10 studies found no significant framing or cognitive effects. Conclusion This systematic review highlights the need to consider robustness of elicited values in choice experiments. It will also guide future choice experiments that aim to estimate inequity aversion.
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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 | 0.160 | 0.450 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".