Hysterical Bodies: A socio-legal and feminist policy analysis of gender bias in the treatment of cardiovascular disease in the US, UK, and Canada
Bibliographic record
Abstract
This thesis is an exploration of gender bias in the treatment of chronic illnesses by analysing the relationship between gender, feminism, and ableism in regard to power relations within a human rights healthcare-focused framework. Through a combination of feminist theory and critical disability theory, this thesis aims to answer how gendered cycles of inequality are perpetuated within healthcare systems specifically in the treatment of cardiovascular disease. Together with the theoretical framework, a socio-legal method and feminist policy analysis are utilized to answer the research questions and analyze healthcare laws and policies from the US, UK, and Canada. This thesis demonstrates how gendered cycles of inequality are perpetuated within healthcare systems by focusing on three intertwined and cyclical aspects: medical research, healthcare inequities, and healthcare accessibility. Additionally, this thesis determines that gender bias is a systemic and structural problem as gender bias is embedded in healthcare, as a result, women are more likely to be dismissed, ignored, misdiagnosed, and receive inappropriate, delayed medical 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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".