The Feminist Approach in the Decision-making Process for Treatment of Women With Breast Cancer
Bibliographic record
Abstract
INTRODUCTION: The principal aim of this review was to investigate a feminist approach to the decision-making process for women with breast cancer. Empirical research into patient preferences for being informed about and participating in healthcare decisions has some limitations because it is mostly quantitative and designed within the dominant medical culture. Indigenous medical knowledge and alternative medical treatments are not widely accepted because of the lack of confirmed efficacy of such treatments in evidence-based literature. While discussing their treatment options with oncologists, women with breast cancer frequently express many concerns regarding treatment side effects, and sometimes decline conventional treatment when the risks are too high. METHODS: A search of all relevant literary sources, including Pub-Med, ERIC, Medline, and the Ontario Institute for Studies in Education at the University of Toronto was conducted. The key words for selection of the articles were "feminism," "decision-making," "patients preferences for treatment," and "breast cancer." RESULTS: Fifty-one literary sources were selected. The review was divided into the following themes: (1) limitations of the patient decision-making process in conventional medicine; (2) participation of native North American patients in healthcare decisions; (3) towards a feminist approach to breast cancer; and (4) towards a feminist theory of breast cancer. CONCLUSION: This article discusses the importance of a feminist approach to the decision-making process for treatment of patients with breast cancer. As the literature suggests, the needs of minority patients are not completely fulfilled in Western medical culture. Introducing feminist theory into evidence-based medicine will help patients to be better informed about treatment choices and will assist them to select treatment according to their own beliefs and values.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".