SMS behaviour change communication and eVoucher interventions to increase uptake of cervical cancer screening in the Kilimanjaro and Arusha regions of Tanzania: a randomised, double-blind, controlled trial of effectiveness
Bibliographic record
Abstract
BACKGROUND: Cervical cancer, although almost entirely preventable through cervical cancer screening (CCS) and human papillomavirus vaccination, is the leading cause of cancer deaths among women in Tanzania. Barriers to attending CCS include lack of awareness of CCS, affordability concerns regarding screening and travel cost. We aimed to compare the effectiveness of SMS (short message service) behaviour change communication (BCC) messages and of SMS BCC messages delivered with a transportation electronic voucher (eVoucher) on increasing uptake of CCS versus the control group. METHODS: Door-to-door recruitment was conducted between 1 February and 13 March 2016 in randomly selected enumeration areas in the catchment areas of two hospitals, one urban and one rural, in Northern Tanzania. Women aged 25-49 able to access a mobile phone were randomised using a computer-generated 1:1:1 sequence stratified by urban/rural to receive either (1) 15 SMS, (2) an eVoucher for return transportation to CCS plus the same SMS, or (3) one SMS informing about the nearest CCS clinic. Fieldworkers and participants were masked to allocation. All areas received standard sensitisation including posters, community announcements and sensitisation similar to community health worker (CHW) sensitisation. The primary outcome was attendance at CCS within 60 days of randomisation. FINDINGS: 281). By day 60 of follow-up, 101 women (11.9%) attended CCS. Intervention group participants were more likely to attend than control group participants (SMS + eVoucher OR: 4.7, 95% CI 2.9 to 7.4; SMS OR: 3.0, 95% CI 1.5 to 6.2). TRIAL REGISTRATION NUMBER: NCT02680613.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".