Asymmetric Power Relations in Gynaecological Consultations for Cervical Cancer Prevention: Biomedical and Gender Issues
Bibliographic record
Abstract
A generic qualitative research, using a poststructuralist feminist perspective, was conducted in a Spanish gynaecology unit with the following aims: (a) to analyse how asymmetric power relations in relation to biomedical knowledge and gender shape the medical encounters between gynaecologists and women diagnosed with cervical intraepithelial neoplasia and (b) to explore the cognitive, moral, and emotional responses expressed by patients. A total of 21 women diagnosed with cervical intraepithelial neoplasia were recruited through purposive sampling. Semi-structured interviews were recorded and transcribed, and a thematic analysis was carried out. Two major themes were identified: (a) gendered relations in cervical intraepithelial neoplasia medical encounters are based on hidden, judgmental moral assumptions, making women feel irresponsible and blamed for contracting the human papillomavirus infection; (b) biomedical power is based on the positivist assumption of a single truth (scientific knowledge), creating asymmetric relations rendering women ignorant and infantilised. Women reacted vehemently during the interviews, revealing a nexus of cognitive, moral, and emotional reactions. In medical encounters for management of cervical intraepithelial neoplasia, patients feel they are being morally judged and given limited information, generating emotional distress. Healthcare professionals should question whether their practices are based on stereotypical gender assumptions which lead to power asymmetries during encounters.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".