Bibliographic record
Abstract
THINKING WOMEN AND HEALTH CARE REFORM IN CANADA Pat Armstrong, Barbara Clow, Karen Grant, Margaret Haworth-Brockman, Beth Jackson, Ann Pederson, and Morgan Seeley, Eds. Toronto: Women's Press, 2012 Thinking Women and Health Care Reform in Canada explores women's roles as both patients and practitioners in the Canadian health care system. Armstrong et al. begin with the premise that although the value of universal health care is established in Canadian society, its unique implications for are rarely addressed in calls for reform. Their study attempts to fill this lacuna by offering a gendered analysis of the organization of Canada's health care system and the social structures necessary to maintain it. By extending their research to include the role of unpaid care work in maintaining Canada's health care system they challenge previously held assumptions about the scope of health care analysis. Written by members of Women and Health Care Reform (WHCR), this book is billed as a legacy project updating more than a decade of their collective research before they disband due to federal budget restructuring. The anthology's coherence belies the individual authors' varied backgrounds; their history of collaboration is evident in the cohesiveness of this work. Each chapter incorporates similar methodological tools and theoretical foundations achieved through the use of four complementary frameworks--feminist political economy, feminist epistemologies, sex- and gender-based analysis, and intersectionality--all of allow for a conception of health that includes both individuals and communities. In so doing, they provide a broad overview of the organization of healthcare in Canada, while highlighting a cross-section of prominent issues in care that would benefit from a gendered analysis, including: residential long-term care, home care, the mental health of health care workers, private health insurance, and obesity. This collection argues that all aspects of health care are, indeed, women's issues. Armstrong et al. grapple with the inherent problem of assuming a single category of women but opt to utilize this term in a strategic capacity, reflecting the use of this category in health policy, while recognizing the unique issues of identity and power that fundamentally divide this group. To this end, they ask not only are the issues for women? in health, but also which are affected in what ways? Woven throughout this collection are references to women's unpaid care work as figuring prominently in the foundation of Canada's health care system. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".