PREFACE Reducing Health Disparities A Priority for Canada
Bibliographic record
Abstract
Despite Canada’s generally high standard of living and despite a system that promis-es universal access to high quality care, disparities in health remain a pressingnational concern. These disparities are not randomly distributed. Specific subpop-ulations suffer a burden of illness and distress greater than other residents of Canada. For this reason, they can be characterized as “vulnerable populations”. Aboriginal peoples, immigrants, refugees, the disabled, the poor, the homeless, people with stigmatizing condi-tions, the elderly, children and youth in disadvantaged circumstances, people with poor lit-eracy skills, and women in precarious circumstances are vulnerable populations – more likely than others to become ill and less likely to receive appropriate care. Despite our commitment to equity and access – in health and opportunity – 18 % of Canadians live in deep poverty, and income inequality is increasing. The wealthy live longer than the poor, and experience fewer chronic illnesses, less obesity, and lower levels of mental distress. According to the 2001 census, at least 14,000 people in Canada are homeless. Homeless people are at risk for premature death, infectious diseases, mental ill-ness and substance abuse. The middle-aged homeless – people in their 40s and 50s – often have health disabilities more commonly seen in individuals who are decades older. Canada’s Aboriginal population is just under 1 million, and its rate of growth is double
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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".