Factors Associated with Intention to Vaccinate Children 0-11 Years of Age Against COVID-19
Bibliographic record
Abstract
BACKGROUND: Millions of children have tested positive for SARS-CoV-2, and over 1000 children have died in the US. However, vaccination rates for children 5 to 11 years old are low. METHODS: Starting in August 2020, we conducted a prospective SARS-CoV-2 household surveillance study in Spanish and English-speaking households in New York City and Utah. From October 21 to 25, 2021, we asked caregivers about their likelihood of getting COVID-19 vaccine for their child, and reasons that they might or might not vaccinate that child. We compared intent to vaccinate by site, demographic characteristics, SARS-CoV-2 infection detected by study surveillance, and parents' COVID-19 vaccination status using Chi-square tests and a multivariable logistic regression model, accounting for within-household clustering. RESULTS: Among parents or caregivers of 309 children (0 to 11 years) in 172 households, 87% were very or somewhat likely to intend to vaccinate their child. The most prevalent reasons for intending to vaccinate were to protect family and friends and the community; individual prevention was mentioned less often. The most prevalent reasons for not intending to vaccinate were side effect concerns and wanting to wait and see.In multivariable analysis, parents had much lower odds of intending to vaccinate if someone in the household had tested SARS-CoV-2-positive during the study (adjusted odds ratio = 0.09; 95% confidence interval, 0.03-0.3). CONCLUSION: This study highlighted several themes for clinicians and public health officials to consider including the importance and safety of vaccination for this age-group even if infected previously, and the benefits of vaccination to protect family, friends, and community.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".