Binary Choice Health State Valuation and Mode of Administration: Head-to-Head Comparison of Online and CAPI
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: Health state valuation exercises can be conducted online, but the quality of data generated is unclear. OBJECTIVE: To investigate whether responses to binary choice health state valuation questions differ by administration mode: online versus face to face. METHODS: Identical surveys including demographic, self-reported health status, and seven types of binary choice valuation questions were administered in online and computer-assisted personal interview (CAPI) settings. Samples were recruited following procedures employed in typical online or CAPI studies. Analysis included descriptive comparisons of the distribution of responses across the binary options and probit regression to explain the propensity to choose one option across modes of administration, controlling for background characteristics. RESULTS: Overall, 422 (221 online; 201 CAPI) respondents completed a survey. There were no overall age or sex differences. Online respondents were educated to a higher level than were the CAPI sample and general population, and employment status differed. CAPI respondents reported significantly better general health and health/life satisfaction. CAPI took significantly longer to complete. There was no effect of the mode of administration on responses to the valuation questions, and this was replicated when demographic differences were controlled. CONCLUSIONS: The findings suggest that both modes may be equally valid for health state valuation studies using binary choice methods (e.g., discrete choice experiments). There are some differences between the observable characteristics of the samples, and the groups may differ further in terms of unobservable characteristics. When designing health state valuation studies, the advantages and disadvantages of both approaches must be considered.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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