Predictors of intent to vaccinate against HPV/cervical cancer: a multi-ethnic survey of 769 parents in New Zealand.
Bibliographic record
Abstract
AIM: To identify factors predictive of parents' intent to have their daughters' receive the HPV/cervical cancer vaccine. METHODS: 3123 questionnaires were distributed to parents recruited from 14 socioeconomically diverse schools in 2008. Survey questions were structured around the health beliefs model. The main outcome measure was intent to seek vaccination for daughter(s). RESULTS: A quarter of parents completed questionnaires (769/3123). Two-thirds of respondents (67%) indicated they would want their daughter(s) to receive the vaccine, with no significant differences by ethnicity. Intent to vaccinate was significantly associated with having fewer negative views on vaccination (OR 0.47, 95%CI 0.37-0.59), having adequate information about the vaccine, perceiving HPV infection and cervical cancer as serious and likely to occur (OR 1.2, 95%CI 1.05-1.36), and considering efficacy and safety of the vaccine important (OR 1.17, 95%CI 1.06-1.28) (p<0.01). Awareness of HPV-related facts was lowest among Maori and Pacific parents (p<0.001). Pacific parents were more likely to have concerns about vaccination impacting negatively on girls' sexual behaviour. IMPLICATIONS: Strategies will be needed to provide detailed information outlining HPV prevalence and consequences, vaccine safety and efficacy to ensure all parents and their daughters are adequately informed when deciding on vaccination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".