Understanding the role of embarrassment in gynaecological screening: a qualitative study from the ASPIRE cervical cancer screening project in Uganda
Bibliographic record
Abstract
OBJECTIVE: To define embarrassment and develop an understanding of the role of embarrassment in relation to cervical cancer screening and self-collected human papillomavirus (HPV) DNA testing in Uganda. DESIGN: Cross-sectional, qualitative study using semistructured one-to-one interviews and focus groups. PARTICIPANTS: 6 key-informant health workers and 16 local women, purposively sampled. Key informant inclusion criteria: Ugandan members of the project team. Focus group inclusion criteria: woman age 30-69 years, Luganda or Swahili speaking, living or working in the target Ugandan community. EXCLUSION CRITERIA: unwillingness to sign informed consent. SETTING: Primary and tertiary low-resource setting in Kampala, Uganda. RESULTS: In Luganda, embarrassment relating to cervical cancer is described in two forms. 'Community embarrassment' describes discomfort based on how a person may be perceived by others. 'Personal embarrassment' relates to shyness or discomfort with her own genitalia. Community embarrassment was described in themes relating to place of study recruitment, amount of privacy in dwellings, personal relationship with health workers, handling of the vaginal swab and misunderstanding of HPV self-collection as HIV testing. Themes of personal embarrassment related to lack of knowledge, age and novelty of the self-collection swab. Overall, embarrassment was a barrier to screening at the outset and diminished over time through education and knowledge. Fatalism regarding cervical cancer diagnosis, worry about results and stigma associated with a cervical cancer diagnosis were other psychosocial barriers described. Overcoming psychosocial barriers to screening can include peer-to-peer education, drama and media campaigns. CONCLUSIONS: Embarrassment and other psychosocial barriers may play a large role at the onset of a screening programme, but over time as education and knowledge increase, and the social norms around screening evolve, its role diminishes. The role of peer-to-peer education and community authorities on healthcare cannot be overlooked and can have a major impact in overcoming psychosocial and social barriers to screening.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".