A Picture is Worth a Thousand Words: The Role of Survey Training Materials in Stated-Preference Studies
Bibliographic record
Abstract
BACKGROUND: Online survey-based methods are increasingly used to elicit preferences for healthcare. This digitization creates an opportunity for interactive survey elements, potentially improving respondents' understanding and/or engagement. OBJECTIVE: Our objective was to understand whether, and how, training materials in a survey influenced stated preferences. METHODS: An online discrete-choice experiment (DCE) was designed to elicit public preferences for a new targeted approach to prescribing biologics ("biologic calculator") for rheumatoid arthritis (RA) compared with conventional prescribing. The DCE presented three alternatives, two biologic calculators and a conventional approach (opt out), described by five attributes: delay to treatment, positive predictive value, negative predictive value, infection risk, and cost saving to the national health service. Respondents were randomized to receive training materials as plain text or an animated storyline. Training materials contained information about RA and approaches to treatment and described the biologic calculator. Background questions included sociodemographics and self-reported measures of task difficulty and attribute non-attendance. DCE data were analyzed using conditional and heteroskedastic conditional logit (HCL) models. RESULTS: In total, 300 respondents completed the DCE, receiving either plain text (n = 158) or the animated storyline (n = 142). The HCL showed the estimated coefficients for all attributes aligned with a priori expectations and were statistically significant. The scale term was statistically significant, indicating that respondents who received plain-text materials had more random choices. Further tests suggested preference homogeneity after accounting for differences in scale. CONCLUSIONS: Using animated training materials did not change the preferences of respondents, but they appeared to improve choice consistency, potentially allowing researchers to include more complex designs with increased numbers of attributes, levels, alternatives or choice sets.
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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 | 0.289 | 0.674 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".