Predictors of Human Papillomavirus Vaccine uptake or intent among parents of preadolescents and adolescents
Bibliographic record
Abstract
Aim: The aim of this study was to examine predictors of human papillomavirus (HPV) vaccine uptake or intent among parents of pre-adolescents and adolescents.Methods: A cross-sectional descriptive study was conducted among parents of girls aged 9 to 18 years, visiting two primary care clinics in central Texas from September to November 2015. Pearson’s product-moment correlation procedures and path analyses based on Health Belief Model were performed.Results: Path analysis showed that provider recommendation for HPV vaccination (β = 0.37; p < .001) and perceived HPV vaccine harm (β = -0.48; p < .001) had statistically significant direct effects on HPV vaccine uptake or intent. The perceived HPV vaccine effectiveness was directly influenced by HPV knowledge (β = 0.39; p < .001), empowerment in parent-provider relationships (β = 0.30; p = .006) and parental college education (β = 0.23; p = .039).}Conclusions: Together with parental empowerment fostering an equal partnership with providers, targeted education to improve parental HPV knowledge may convince them of the HPV vaccine effectiveness. This, in turn, may help them put the perceived HPV vaccine harm in proper perspective and allow them to make informed decisions regarding the timely HPV vaccination of their children. Because provider recommendation is one of the most important contributing factors for HPV vaccine uptake or intent, parental education and recommendations from nurses will help reduce the knowledge gaps and empower parents to make the timely decisions to vaccinate their children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".