Predictors of Intention to Vaccinate or Continue to Vaccinate Children Against SARS-CoV-2 During the Fifth Wave of the COVID-19 Pandemic in the USA
Bibliographic record
Abstract
The Centre for Disease Control recommends vaccination of children against SARS-CoV-2 to reduce the severity of COVID-19 disease and reduce the likelihood of associated complications. Vaccination of children requires the consent of parents or guardians, and levels of consent may ebb and flow over the course of the pandemic. This exploratory study examines predictors of parental intentions to vaccinate their children and the speed with which they would have them vaccinated during the fifth wave of the pandemic when vaccines were just being approved for use in children using a convenience sample of 641 parents reporting on 962 children. Multi-level regression analyses demonstrated regional differences in likelihood, with those in the Northeast reporting higher likelihood than those in the West. Parents with a conservative belief system were less likely to want to have their children vaccinated. Parents were more likely to have their child vaccinated if the child had COVID-19-related health risks, their child had a more complete vaccination history, and COVID-19 was perceived to be a greater threat to oneself and one's family. Faster intended vaccination speed was associated with regional urbanicity, liberal belief systems, more complete vaccination histories, and parental COVID-19 vaccination history. Higher levels of parental anxiety and lower levels of perceived vaccine danger were associated with increased speed. The severity of the COVID-19 pandemic within one's county was marginally related to speed, but not likelihood. These results underscore the importance of regular assessment of parental intentions across the pandemic, for practitioners to probe parental anxiety levels when discussing vaccination, to explicitly address risk/benefit analyses when communicating with parents, and to target previously routine unvaccinated parents and those in more rural areas to increase vaccine uptake. Comparisons are made with Galanis et al.'s (2022) recent meta-analysis on the topic.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".