Coronavirus disease 2019 vaccination among young children: Associations with fathers’ and mothers’ influenza vaccination status
Bibliographic record
Abstract
Objectives: To examine the association between parents' influenza vaccination and their children's coronavirus disease 2019 (COVID-19) vaccination status. Methods: , a cohort of fathers and their co-parents living in the United States. Parents' influenza vaccination status and children's COVID-19 vaccination status were reported from June 2022-July 2023. Logistic regression was used to examine the association between parental influenza vaccination (both parents vs. neither parent vs. mother only vs. father only vaccinated) and child COVID-19 vaccination (received at least 1 vs. 0 doses). Models were adjusted for recruitment site, income, parent education, child race/ethnicity, child age, and childcare enrollment. Inverse probability weighting was used to account for selection bias into the father-mother dyad sample. Results: Children were predominately non-Hispanic White (56 %) and aged 3-5 years (62 %). In most households, both parents (64 %) received the influenza vaccine and half (53 %) of children received the COVID-19 vaccine. One-in-four fathers (23 %) lacked knowledge about their child's COVID-19 vaccination eligibility. Compared to children with two unvaccinated parents, having only their father (adjusted odds ratio [AOR] = 2.84, 95 % confidence interval [CI]: 1.52-5.36), only their mother (AOR = 4.04, 95 % CI: 2.16-7.68), and both parents (AOR = 10.33, 95 % CI: 6.29-17.53) vaccinated against influenza was associated with higher odds of children receiving the COVID-19 vaccine. Conclusions: Father and mother influenza vaccination is associated with child COVID-19 vaccination. Given many fathers were unaware their child was eligible for the COVID-19 vaccine, it is critical to tailor vaccine messaging for fathers.
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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 | 0.000 | 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".