How Do People Attribute Income-Related Inequalities in Health? A Cross-Sectional Study in Ontario, Canada
Bibliographic record
Abstract
CONTEXT: Substantive equity-focused policy changes in Ontario, Canada have yet to be realized and may be limited by a lack of widespread public support. An understanding of how the public attributes inequalities can be informative for developing widespread support. Therefore, the objectives of this study were to examine how Ontarians attribute income-related health inequalities. METHODS: We conducted a telephone survey of 2,006 Ontarians using random digit dialing. The survey included thirteen questions relevant to the theme of attributions of income-related health inequalities, with each statement linked to a known social determinant of health. The statements were further categorized depending on whether the statement was framed around blaming the poor for health inequalities, the plight of the poor as a cause of health inequalities, or the privilege of the rich as a cause of health inequalities. RESULTS: There was high agreement for statements that attributed inequalities to differences between the rich and the poor in terms of employment, social status, income and food security, and conversely, the least agreement for statements that attributed inequalities to differences in terms of early childhood development, social exclusion, the social gradient and personal health practices and coping skills. Mean agreement was lower for the two statements that suggested blame for income-related health inequalities lies with the poor (43.1%) than for the three statements that attributed inequalities to the plight of the poor (58.3%) or the eight statements that attributed inequalities to the privilege of the rich (58.7%). DISCUSSION: A majority of this sample of Ontarians were willing to attribute inequalities to the social determinants of health, and were willing to accept messages that framed inequalities around the privilege of the rich or the plight of the poor. These findings will inform education campaigns, campaigns aimed at increasing public support for equity-focused public policy, and knowledge translation strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".