Knowledge and acceptance of HPV vaccination and its associated factors among parents of daughters in Addis Ababa, Ethiopia: a community-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cervical cancer is one of the most diagnosed and deadly cancers in women globally. Though vaccination is an effective way to reduce cervical cancer, people's knowledge and acceptance of the vaccination remains a challenge in low and middle-income countries. Therefore, the aim of this study was to assess the level of knowledge and acceptance of Human Papilloma Virus (HPV) vaccination and its associated factors among parents of daughters in Addis Ababa, Ethiopia. METHODS: A community-based cross-sectional study was conducted among parents or guardians whose daughters are in the age group of 9-17 years and residing in Akaki-Kalty sub-city in Addis Ababa. A multistage sampling technique was used to select the study participants (n = 430). Face-to-face interview was conducted by using a structured questionnaire. Factors associated with the acceptance of HPV vaccination was identified by multivariable binary logistic regression and expressed by adjusted odds ratio (aOR), and respective 95% confidence interval. RESULTS: Complete response was obtained from 422 (98.1%) of the participants and their mean age was 39.0 years (SD ± 9.9). Out of the study participants, 41.7% and 72.0% had poor knowledge on cervical cancer and HPV, respectively. More than a quarter (27.0%) of the participants has never heard about HPV vaccine. One-third (36.5%) of the participants had negative attitude towards the HPV vaccine. Overall, 94.3% of the study participants were willing to vaccinate their daughters for HPV. Vaccine acceptability was associated with higher monthly income (aOR = 2.48, 95% CI 1.08-6.34), good knowledge on HPV (aOR = 2.32, 95% CI 1.56-4.87) and the vaccine (aOR = 2.24, 95% CI 1.12-8.60), and positive attitude towards the vaccine (aOR = 5.03, 95% CI 1.63-9.56). CONCLUSIONS: The overall HPV vaccine acceptance was high. However, two out of five and one-thirds of the parents had poor knowledge on cervical cancer and negative attitude towards the HPV vaccine, respectively. Higher monthly income, good knowledge on HPV and the vaccine, and positive attitude towards the vaccine were associated with acceptance of HPV vaccination. To ensure sustainable acceptance of HPV vaccination, it is crucial to increase the community awareness in a sustainable manner.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".