Public awareness of income-related health inequalities in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Continued action is needed to tackle health inequalities in Canada, as those of lower income continue to be at higher risk for a range of negative health outcomes. There is arguably a lack of political will to implement policy change in this respect. As a result, we investigated public awareness of income-related health inequalities in a generally representative sample of Ontarians in late 2010. METHODS: Data were collected from 2,006 Ontario adults using a telephone survey. The survey asked participants to agree or disagree with various statements asserting that there are or are not health inequalities in general and by income in Ontario, including questions pertaining to nine specific conditions for which inequalities have been described in Ontario. A multi-stage process using binary logistic regression determined whether awareness of health inequalities differed between participant subgroups. RESULTS: Almost 73% of this sample of Ontarians agreed with the general premise that not all people are equally healthy in Ontario, but fewer participants were aware of health inequalities between the rich and the poor (53%-64%, depending on the framing of the question). Awareness of income-related inequalities in specific outcomes was considerably lower, ranging from 18% for accidents to 35% for obesity. CONCLUSIONS: This is the first province-wide study in Canada, and the first in Ontario, to explore public awareness on health inequalities. Given that political will is shaped by public awareness and opinion, these results suggest that greater awareness may be required to move the health equity agenda forward in Ontario. There is a need for health equity advocates, physicians and researchers to increase the effectiveness of knowledge translation activities for studies that identify and explore health inequalities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".