Can opportunities be enhanced for vaccinating children in home visiting programs? A population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Home visiting programs focused on improving early childhood environments are commonplace in North America. A goal of many of these programs is to improve the overall health of children, including promotion of age appropriate vaccination. In this study, population-based data are used to examine the effect of a home visiting program on vaccination rates in children. METHODS: Home visiting program data from Manitoba, Canada were linked to several databases, including a provincial vaccination registry to examine vaccination rates in a cohort of children born between 2003 and 2009. Propensity score weights were used to balance potential confounders between a group of children enrolled in the program (n = 4,562) and those who were eligible but not enrolled (n = 5,184). Complete and partial vaccination rates for one and two year old children were compared between groups, including stratification into area-level income quintiles. RESULTS: Complete vaccination rates from birth to age 1 and 2 were higher for those enrolled in the Families First program [Average Treatment Effect Risk Ratio (ATE RR) 1.06 (95 % CI 1.03-1.08) and 1.10 (95 % CI 1.05-1.15) respectively]. No significant differences were found between groups having at least one vaccination at age 1 or 2 [ATE RR 1.01 (95 % CI 1.00-1.02) and 1.00 (95 % CI 1.00-1.01) respectively). The interaction between program and income quintiles was not statistically significant suggesting that the program effect did not differ by income quintile. CONCLUSIONS: Home visiting programs have the potential to increase vaccination rates for children enrolled, despite limited program content directed towards this end. Evidence-based program enhancements have the potential to increase these rates further, however more research is needed to inform policy makers of optimal approaches in this regard, especially with respect to cost-effectiveness.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".