Report summary – The Direct Economic Burden of Socioeconomic Health Inequalities in Canada: An Analysis of Health Care Costs by Income Level
Bibliographic record
Abstract
Canadian research indicates that individuals with lower incomes, less education or lower occupational skill levels tend to be less healthy than those who enjoy greater advantages in these areas. This uneven distribution of health across different socioeconomic status (SES) groups is referred to as "socioeconomic inequality in health." Evidence of the economic cost of health inequalities helps us understand the benefits of reducing these inequalities. However, the data needed to generate such evidence is difficult to obtain. A lack of Canadian data linking health costs and socioeconomic characteristics means that assessment of the degree to which health costs are associated with socioeconomic inequalities at the national level is limited. In order to build evidence on the cost of socioeconomic health inequalities, the Public Health Agency of Canada worked with Statistics Canada to test the feasibility of a "bottom-up" approach to compiling national health cost data. A bottom-up approach relies on individual-level data, which allows costs to be calculated by individual-level characteristics not always found in other data sources. This includes indicators of SES such as level of education or income. In this study, the population was divided into quintiles based on income, and the health care costs incurred by these five income groups were examined for a single year (2007-2008).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".