Breast Cancer Care in California and Ontario: Primary Care Protections Greatest Among the Most Socioeconomically Vulnerable Women Living in the Most Underserved Places
Bibliographic record
Abstract
BACKGROUND: Better health care among Canada's socioeconomically vulnerable versus America's has not been fully explained. We examined the effects of poverty, health insurance and the supply of primary care physicians on breast cancer care. METHODS: We analyzed breast cancer data in Ontario (n = 950) and California (n = 6300) between 1996 and 2000 and followed until 2014. We obtained socioeconomic data from censuses, oversampling the poor. We obtained data on the supply of physicians, primary care and specialists. The optimal care criterion was being diagnosed early with node negative disease and received breast conserving surgery followed by adjuvant radiation therapy. RESULTS: Women in Ontario received more optimal care in communities well supplied by primary care physicians. They were particularly advantaged in the most disadvantaged places: high poverty neighborhoods (rate ratio = 1.65) and communities lacking specialist physicians (rate ratio = 1.33). Canadian advantages were explained by better health insurance coverage and greater primary care access. CONCLUSIONS: Policy makers ought to ensure that the newly insured are adequately insured. The Medicaid program should be expanded, as intended, across all 50 states. Strengthening America's system of primary care will probably be the best way to ensure that the Affordable Care Act's full benefits are realized.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".