Validity, Reliability, and Framing Effects in Equity-Efficiency Trade-Off Studies: A Systematic Review
Bibliographic record
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 May 2025 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: 122 equity-efficiency trade-off studies were identified, of which 33 studies (27%) investigated validity, reliability, and/or framing effects. Seventeen (13.9%) studies assessed validity, 10 (8.2%) studies assessed reliability, and 11 (9.0%) assessed framing or cognitive effects. Validity was most frequently assessed by comparing results with hypothesized expectations, whereas 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. Twenty-three of 27 studies reported high or acceptable validity or reliability, and 6 of 11 studies found no significant framing or cognitive effects. CONCLUSIONS: This article identifies key methodological challenges and considerations that can inform the design of future choice experiments estimating 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 | 0.173 | 0.467 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".