Evaluating Group Versus Individual Recruitment Strategies to Improve HPV-Self Collection Amongst Women in Kilimanjaro, Tanzania—‘Dada’ Study
Bibliographic record
Abstract
PURPOSE Tanzania ranks fifth in cervical cancer disease burden globally, with 9,772 new cases and 6,695 deaths reported each year. In the newest version of Tanzania's national cervical cancer strategy, the Ministry of Health recommends that HPV DNA testing is integrated with the national cervical cancer prevention program, to align with the WHO screening recommendations. Recruitment to screening programs remains a challenge in low- and middle-income countries, and there is little evidence to inform recruitment strategies that might improve attendance rates, especially in Tanzania. Results from a systematic review of strategies to improve health and medical research suggest that involving community groups in the research process and ensuring that content and delivery of the intervention is socially acceptable, can improve the likelihood of uptake. The ‘Dada’ study will be conducted, as part of the national cervical cancer prevention program. METHODS The study aims to recruit 1,500 women (age 25-49 if HIV+ or 30-49 if HIV status unknown) in the Kilimanjaro region to evaluate a hybrid-2 evaluation of intervention and implementation effectiveness (e.g., acceptability, feasibility, and fidelity) with cluster randomization of communities to two arms, each testing a different method of recruitment: A) individual, opportunistic recruitment in public settings and B) recruitment through structured community meetings. Recruitment will be performed by community healthcare workers from 30 communities using the two strategies. In both arms, an educational video (‘Dada’) will be delivered to women to encourage HPV self-collection. RESULTS To compare individual and group recruitment strategies in terms of HPV self-collection uptake. This will be examined through various demographic factors, such as age, marital status, education, income, insurance, previous cervical cancer screening, and geographic location (rural, semi-rural or urban). CONCLUSION There is a critical need to generate further evidence for implementation of effective and sustainable strategies to optimize cervical cancer screening and increase the number of women that receive HPV self-collection. The results of our study will also generate informed evidence regarding the optimized strategy to improve the cervical screen-triage-treat approach in Tanzania. The knowledge gained will be disseminated to other African countries with a similar cancer-health disparity.
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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 | 0.045 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".