When risk becomes illness: The personal and social consequences of cervical intraepithelial neoplasia medical surveillance
Bibliographic record
Abstract
BACKGROUND: After the early detection of cervical intraepithelial neoplasia (CIN), medical surveillance of the precancerous lesions is carried out to control risk factors to avoid the development of cervical cancer. OBJECTIVE: To explore the effects of medical surveillance on the personal and social lives of women undergoing CIN follow-up and treatment. METHODOLOGY: A generic qualitative study using a poststructuralist perspective of risk management was carried out in a gynecology clinic in a public hospital of the Galician Health Care System (Spain). Participants were selected through purposive sampling. The sample consisted of 21 women with a confirmed diagnosis of CIN. Semistructured interviews were recorded and transcribed, and a thematic analysis was carried out, including researcher triangulation to verify the results of the analysis. FINDINGS: Two main themes emerged from the participants' experiences: CIN medical surveillance encounters and risk management strategies are shaped by the biomedical discourse, and the effects of "risk treatment" for patients include (a) profound changes expected of patients, (b) increased patient risk management, and (c) resistance to risk management. While doctors' surveillance aimed to prevent the development of cervical cancer, women felt they were sick because they had to follow strict recommendations over an unspecified period of time and live with the possibility of a life-threatening disease. Clinical risk management resulted in the medicalization of women's personal and social lives and produced great uncertainty. CONCLUSIONS: This study is the first to conceptualize CIN medical surveillance as an illness experience for patients. It also problematizes the effects of preventative practices in women's lives. Patients deal with great uncertainty, as CIN medical surveillance performed by gynecologists simultaneously trivializes the changes expected of patients and underestimates the effects of medical recommendations on patients' personal wellbeing and social relations.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".