Addressing health inequities in Ontario, Canada: what solutions do the public support?
Bibliographic record
Abstract
BACKGROUND: As public opinion is an important part of the health equity policy agenda, it is important to assess public opinion around potential policy interventions to address health inequities. We report on public opinion in Ontario about health equity interventions that address the social determinants of health. We also examine Ontarians' support and predictors for targeted health equity interventions versus universal interventions. METHODS: We surveyed 2,006 adult Ontarians through a telephone survey using random digit dialing. Descriptive statistics assessed Ontarians' support for various health equity solutions, and a multinomial logistic regression model was built to examine predictors of this support across specific targeted and broader health equity interventions focused on nutrition, welfare, and housing. RESULTS: There appears to be mixed opinions among Ontarians regarding the importance of addressing health inequities and related solutions. Nevertheless, Ontarians were willing to support a wide range of interventions to address health inequities. The three most supported interventions were more subsidized nutritious food for children (89%), encouraging more volunteers in the community (89%), and more healthcare treatment programs (85%). Respondents who attributed health inequities to the plight of the poor were generally more likely to support both targeted and broader health equity interventions, than neither type. Political affiliation was a strong predictor of support with expected patterns, with left-leaning voters more likely to support both targeted and broader health equity interventions, and right-leaning voters less likely to support both types of interventions. CONCLUSIONS: Findings indicate that the Ontario public is more supportive of targeted health equity interventions, but that attributions of inequities and political affiliation are important predictors of support. The Ontario public may be accepting of messaging around health inequities and the social determinants of health depending on how the message is framed (e.g., plight of the poor vs. privilege of the rich). These findings may be instructive for advocates looking to raise awareness of health inequities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".