Strategies for Community Education Prior to Clinical Trial Recruitment for a Cervical Cancer Screening Intervention in Uganda
Bibliographic record
Abstract
INTRODUCTION: Community engagement and education can improve acceptability and participation in clinical trials conducted in Kisenyi, Uganda. In preparation for a randomized controlled trial exploring different methods for cervical cancer screening, we explored optimal engagement strategies from the perspective of community members and health professionals. METHODS: We conducted key informant interviews followed by serial community forums with purposeful sampling and compared the perspectives of women in Kisenyi (N = 26) to health-care workers (HCW) at the local and tertiary care center levels (N = 61) in a participatory, iterative process. RESULTS: Key themes identified included format, content, language, message delivery, and target population. Women in Kisenyi see demonstration as a key part of an educational intervention and not solely a didactic session, whereas health professionals emphasized the biomedical content and natural history of cervical cancer. Using local language and lay leaders with locally accessible terminology was more of a priority for women in Kisenyi than clinicians. Simple language with a clear message was essential for both groups. Localization of language and reciprocal communication using demonstration between community members and HCW was a key theme. CONCLUSION: Although perceptions of the format are similar between women and HCW, the content, language, and messaging that should be incorporated in a health education strategy differ markedly. The call for lay leaders to participate in health promotion is a clear step toward transforming this cervical cancer screening project to be a fully participatory process. This is important in scaling up cervical cancer screening programs in Kisenyi and will be central in developing health education interventions for this purpose.
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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 | 0.337 | 0.354 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".