Effect of a Health Communication Strategy on Uptake of Cervical Cancer Screening in Isiolo County, Kenya
Bibliographic record
Abstract
Background: To assess the effect of a health communication strategy on women’s community-level uptake of cervical cancer screening in Isiolo County. Purpose: to determine the effect of a health communication strategy on women’s community-level uptake of cervical cancer screening in Isiolo County. Methodology: The study adopted a community-based cluster randomized trial design. Multi-stage sampling was used to derive the sample size. There were 444 women overall, varying in age from 15 to 65 years. Community Health Volunteers disseminated health information to the intervention arm of study and referred participants to link health facilities for screening. An interviewer-administered questionnaire was used for data collection. The research was done between February and August of 2022. Findings: At baseline, the study findings showed that 18.2% of respondents had ever been screened. Reasons for not screening included: fear (12%); feeling healthy (17%) among others. At post-intervention, the cervical cancer screening uptake among the respondents in the intervention arm was found to have increased from 18.2% to 45.9%, while that of the control arm remained at 18%. Respondents in the study’s intervention arm had a 3.867 higher chances of being screened than respondents in the control arm (OR 3.849, CI.1.802- 8.223, P<0.001) Conclusion: At baseline, the screening uptake for cancer of the cervix was low. The existing communication strategies in Isiolo County were limited in addressing cervical cancer. Targeted health communication on cervical cancer screening by Community Health Volunteers, subsequently cervical cancer screening uptake post-intervention.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".