Measuring aversion to health inequality in Canada: an equity-efficiency trade-off experiment
Bibliographic record
Abstract
Abstract OBJECTIVES To estimate the extent to which Canadians are averse to health inequalities, a critical component for equity-informative economic evaluations but lacking in the Canadian context. METHODS We conducted three experiments among a representative sample of adults living in Canada to elicit value judgements about reducing income-related health inequality vs. improving population health. Each experiment compared two programs: (Experiment 1) universal and tailored vaccination; (Experiment 2) non-specific prevention programs; (Experiment 3) generic health care programs. The programs varied in terms of efficiency (additional life years), and income-related health inequality. Preferences were elicited using benefit-trade off analysis and were classified as: 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). RESULTS We recruited 1,000 participants per experiment. Preferences for the vaccination, prevention, and generic experiments were distributed as follows: pro-rich (Atkinson Index<0): 31%, 22%, and 16% respectively; health maximizers (Atkinson Index=0): 2%, 3%, and 2%, respectively; weighted prioritarians (Atkinson Index>0): 13%, 19%, and 22% respectively; maximins (Atkinson Index=∞): 0%, 1%, and 3%, respectively; egalitarian (Atkinson Index undefined): 54%, 55%, and 57%, respectively. The median responses reflected a preference for minimizing health inequalities across the three experiments. CONCLUSIONS Our findings suggest a strong aversion to health inequality among people living in Canada with over half of respondents willing to minimize health inequalities regardless of the impact on efficiency.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".