Trying to Heal Without “Bringing Problems”: Navigating Cervical Precancer Stigma in a Phase I Clinical Trial in Western Kenya
Bibliographic record
Abstract
Background: Cervical cancer remains a leading cause of cancer-related deaths among women in low- and middle-income countries, particularly in sub-Saharan Africa. Sociocultural barriers including stigma significantly influence women's decisions around screening and treatment. Objective: To explore how stigma influences women's experiences with cervical precancer care in the context of emerging self-administered topical therapies. Design: A qualitative approach using convenience sampling. Methods: Seventeen in-depth interviews were conducted between 2024 and 2025 in Kisumu, Kenya, with women who had been diagnosed with cervical precancer. Participants were recruited to self-administer intravaginal artesunate in a Phase I clinical trial, and interviews explored their experiences. Thematic analysis was conducted using the Health Stigma and Discrimination Framework. Results: All women discussed experiences indicative of enacted, anticipated, and internalized stigmas related to partner blame, disclosure, and imagining the diagnosis as an imminent death sentence. These stigmas were driven and facilitated by poverty and limited health care access, embarrassment around sexual health, community fears of cervical cancer death, patriarchal norms, and cervical precancer's disproportionate burden on women. Women with cervical precancer feel "marked" by medication usage, clinic visits, community expectations of a "cancer" appearance, or painful sexual intercourse. By exercising relational autonomy, women in this trial were able to navigate stigmas around sexual health and overcome the debilitating fear of death. They were ultimately able to complete the self-administered artesunate treatment course and initiate conversations in their communities to address stigmas around cervical precancer treatment. Nevertheless, stigmas caused delays to screening and treatment among some participants, while others experienced intimate partner violence or conflict during artesunate treatment. Conclusion: Stigmas around cervical precancer, artesunate usage, and abstinence requirements can deeply impact women's social experiences, intimate partnerships, and mental health. Future trials of self-administered therapies should integrate stigma reduction strategies developed in collaboration with participants and survivors.ClinicalTrials.gov NCT06165614, https://clinicaltrials.gov/study/NCT06165614.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".