Awareness of cervical cancer and willingness to be vaccinated against human papillomavirus in Mozambican adolescent girls
Bibliographic record
Abstract
Sub-Saharan Africa concentrates the largest burden of cervical cancer worldwide. The introduction of the HPV vaccination in this region is urgent and strategic to meet global health targets. This was a cross-sectional study conducted in Mozambique prior to the first round of the HPV vaccine demonstration programme. It targeted girls aged 10-19 years old identified from schools and households. Face-to-face structured interviews were conducted. A total of 1147 adolescents were enrolled in three selected districts of the country. Most girls [84% (967/1147)] had heard of cervical cancer, while 76% believed that cervical cancer could be prevented. However only 33% (373/1144) of girls recognized having ever heard of HPV. When girls were asked whether they would accept to be vaccinated if a vaccine was available in Mozambique, 91% (1025/1130) answered positively. Girls from the HPV demonstration districts showed higher awareness on HPV and cervical cancer, and willingness to be vaccinated. This study anticipates high acceptability of the HPV vaccine in Mozambique and high awareness about cervical cancer, despite low HPV knowledge. These results highlight that targeted health education programmes are critical for acceptance of new tools, and are encouraging for the reduction of cervical cancer related mortality and morbidity in Mozambique.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".