Effect of CARD (Comfort-Ask-Relax-Distract) on acceptance of school-based vaccinations: A controlled before and after study
Bibliographic record
Abstract
Injection-related pain and fear are common in children undergoing school-based vaccinations and contribute to vaccine refusal. The CARD system (Comfort Ask Relax Distract) includes interventions that reduce pain and fear. This pragmatic study evaluated CARD’s impact on school program-related vaccine uptake using a controlled before and after study design. Altogether, 67 Public and Catholic schools receiving vaccination services from a public health unit in Ontario, Canada, were included. Schools were ranked by vaccination uptake in grade 7 students in 2022–2023. The bottom 29 schools were allocated to CARD (intervention) and the remainder to control for 2023–2024 school vaccinations. In CARD schools, nurses educated students at school and information about CARD was added to vaccine correspondence for parents. On vaccination day, nurses applied interventions designed to improve coping. Vaccination coverage outcomes were assessed for 3 targeted vaccines – human papillomavirus (HPV), hepatitis B (HB), and meningococcal conjugate-ACYW (MCV). Baseline (2022–2023) vaccine coverage was lower (p < .01) in CARD-assigned schools (n = 1478) [vs. controls (n = 1729)] for all vaccines. During the study period (2023–2024), coverage increased (p < .001) within the CARD group (n = 1403) for HPV (+9.8%), HB (+8.1%), and MCV (+5.3%). Coverage remained the same or decreased within the control group (n = 1877): HPV (−1.1%; p = .50), HB (−4.4%; p = .004), and MCV (−2.7%; p = .03). In 2023–2024, vaccination uptake did not significantly differ between groups. Overall uptake in all schools was higher in 2023–2024 vs. 2022–2023 for HPV (+4.0%; p = .001). This study demonstrates effectiveness of CARD for increasing vaccination uptake. Evaluation of impact in other geographical regions is warranted.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".