E‐learning and health inequality aversion: A questionnaire experiment
Bibliographic record
Abstract
In principle, questionnaire data on public views about hypothetical trade-offs between improving total health and reducing health inequality can provide useful normative health inequality aversion parameter benchmarks for policymakers faced with real trade-offs of this kind. However, trade-off questions can be hard to understand, and one standard type of question finds that a high proportion of respondents-sometimes a majority-appear to give exclusive priority to reducing health inequality. We developed and tested two e-learning interventions designed to help respondents understand this question more completely. The interventions were a video animation, exposing respondents to rival points of view, and a spreadsheet-based questionnaire that provided feedback on implied trade-offs. We found large effects of both interventions in reducing the proportion of respondents giving exclusive priority to reducing health inequality, though the median responses still implied a high degree of health inequality aversion and-unlike the video-the spreadsheet-based intervention introduced a substantial new minority of non-egalitarian responses. E-learning may introduce as well as avoid biases but merits further research and may be useful in other questionnaire studies involving trade-offs between conflicting values.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".