Health insurance coverage for breast cancer care: a feminist political economy perspective on women's experiences in Ontario and New York
Bibliographic record
Abstract
This thesis examines how the health insurance systems of Ontario and New York impact women's health insurance experiences in relation to breast cancer care. The analysis provided draws on insights from feminist political economy scholarship to consider the roles of states and markets as well as households and voluntary sectors in health insurance coverage. Women's experiences in Ontario-where public health insurance plays a primary role-and in New York-where private health insurance plays a primary role-are addressed against the background of welfare state transformation and neoliberal reform reaching beyond jurisdictional boundaries. Review of secondary literature, legislation and policy documents establishes the context for analysis of 42 semi-structured interviews conducted with women diagnosed with breast cancer in the neighboring jurisdictions of Lanark and Leeds Grenville in Ontario and St. Lawrence County in New York. \n \nThematic analysis of the interviews conducted identifies three overarching themes: 'commodified coverage', 'responsibilized individuals' and 'gradation in consequences'. With the primacy of private health insurance in New York's health insurance system, participants' narratives are found to reflect more commodified coverage, more responsibilized individuals, and greater gradation in the consequences of financing breast cancer care than in Ontario, where public health insurance plays a primary role. This thesis underlines the importance of health insurance coverage as a women's issue and highlights the importance of public policy in shaping the conditions under which women use health insurance to finance breast cancer care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".