Measuring Aversion to Income-Related Health Inequality in Canada: An Equity-Efficiency Trade-Off Experiment
Bibliographic record
Abstract
OBJECTIVES: To estimate the extent to which people living in Canada are averse to income-related health inequalities, a critical component for equity-informative economic evaluations but lacking in the Canadian context. METHODS: We conducted 3 experiments among a sample of adults living in Canada to elicit value judgements about reducing income-related health inequality versus improving population health. Each experiment compared 2 programs: (experiment 1) universal and tailored vaccination, (experiment 2) nonspecific prevention programs, and (experiment 3) generic healthcare programs. The programs varied in terms of efficiency (additional life-years), and health inequality across income groups. Preferences were elicited using benefit trade-off analysis and were classified as follows: pro-rich (maximizing the health of individuals with the highest income), health maximizer (maximizing total health), weighted prioritarian (willing to trade some health to reduce inequalities), maximin (only improving the health of the individuals with the lowest income), and egalitarian (minimizing health inequalities at all costs). RESULTS: We recruited 1000 participants per experiment. Preferences for the vaccination, prevention, and generic experiments were distributed as follows: pro-rich (aversion parameter <0): 31%, 22%, and 16%, respectively; health maximizers (aversion parameter = 0): 2%, 3%, and 2%, respectively; weighted prioritarians (aversion parameter > 0): 13%, 19%, and 22%, respectively; maximins (aversion parameter = ∞): 0%, 1%, and 3%, respectively; and egalitarian (aversion parameter undefined): 54%, 55%, and 57%, respectively. The median responses reflected a preference for minimizing income-related health inequalities across the 3 experiments. CONCLUSIONS: Our findings suggest a strong aversion to income-related health inequality among the respondents with more than half being classified as egalitarians.
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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 | 0.040 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".