Can a moral reasoning exercise improve response quality to surveys of healthcare priorities?
Bibliographic record
Abstract
OBJECTIVE: To determine whether a moral reasoning exercise can improve response quality to surveys of healthcare priorities METHODS: A randomised internet survey focussing on patient age in healthcare allocation was repeated twice. From 2574 internet panel members from the USA and Canada, 2020 (79%) completed the baseline survey and 1247 (62%) completed the follow-up. We elicited respondent preferences for age via five allocation scenarios. In each scenario, a hypothetical health planner made a decision to fund one of two programmes identical except for average patient age (35 vs 65 years). Half of the respondents (intervention group) were randomly assigned to receive an additional moral reasoning exercise. Responses were elicited again 7 weeks later. Numerical scores ranging from -5 (strongest preference for younger patients) to +5 (strongest preference for older patients); 0 indicates no age preference. Response quality was assessed by propensity to choose extreme or neutral values, internal consistency, temporal stability and appeal to prejudicial factors. RESULTS: With the exception of a scenario offering palliative care, respondents preferred offering scarce resources to younger patients in all clinical contexts. This preference for younger patients was weaker in the intervention group. Indicators of response quality favoured the intervention group. CONCLUSIONS: Although people generally prefer allocating scarce resources to young patients over older ones, these preferences are significantly reduced when participants are encouraged to reflect carefully on a wide range of moral principles. A moral reasoning exercise is a promising strategy to improve response quality to surveys of healthcare priorities.
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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 | 0.268 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads 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".