Social Media Perceptions and Internet Verification Skills Associated With Human Papillomavirus Vaccine Decision-Making Among Parents of Children and Adolescents: Cross-sectional Survey
Bibliographic record
Abstract
BACKGROUND: Human Papillomavirus (HPV) vaccination is recommended for children aged 11-12 years in the United States. One factor that may contribute to low national HPV vaccine uptake is parental exposure to misinformation on social media. OBJECTIVE: This study aimed to examine the association between parents' perceptions of the HPV vaccine information on social media and internet verification strategies used with the HPV vaccine decision-making stage for their child. METHODS: Parents of children and adolescents aged 9-17 years were recruited for a cross-sectional survey in North Texas (n=1192) and classified into 3 groups: children and adolescents who (1) were vaccinated, (2) unvaccinated and did not want the vaccine, and (3) unvaccinated and wanted the vaccine. Multinomial logistic regression models were estimated to identify factors associated with the HPV vaccine decision-making stage with children and adolescents who were vaccinated as the referent group. RESULTS: Of the 1192 respondents, 44.7% (n=533) had an HPV-vaccinated child, 38.8% (n=463) had an unvaccinated child and did not want the vaccine, and 16.4% (n=196) had an unvaccinated child and wanted the vaccine. Respondents were less likely to be "undecided/not wanting the vaccine" if they agreed that HPV information on social media is credible (adjusted odds ratio [aOR] 0.40, 95% CI 0.26-0.60; P=.001), disagreed that social media makes them question the HPV vaccine (aOR 0.22, 95% CI 0.15-0.33; P<.001), or had a higher internet verification score (aOR 0.74, 95% CI 0.62-0.88; P<.001). CONCLUSIONS: Interventions that promote web-based health literacy skills are needed so parents can protect their families from misinformation and make informed health care decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".