Stakeholder feedback on The CARD™ System to improve the vaccination experience at school
Bibliographic record
Abstract
OBJECTIVE: School-based vaccination programs can be a source of distress for many students due to the pain from the needle injection and related fears. We created a multifaceted Knowledge Translation (KT) intervention to address vaccination and pain, fear, and fainting called The CARD™ System. The objectives were to document acceptability of key tools included in the multifaceted KT intervention and their effectiveness in improving knowledge and attitudes about vaccination pain and fear. METHODS: Quantitative and qualitative methods were used. Students, school staff, public health nurses, and parents participated in separate focus groups whereby they independently completed a knowledge and attitudes survey and provided structured and qualitative feedback on key KT tools of the multifaceted KT intervention. They then repeated the knowledge and attitudes survey. RESULTS: Altogether, 22 students (grade 6 and 7), 16 school staff (principals, grade 7 and 8 teachers, resource teachers, secretaries), 10 nurses (injecting, charge, and school nurses), and 3 parents participated. Knowledge test scores increased post-KT tool review: 8.5 (2.1) versus 7.3 (1.9); P<0.001. Attitudes were more positive about the individual nature of pain and fear experience during vaccination. Student fear scores were lower post-tool review: 5.1 (2.9) versus 4.6 (3.0); P<0.001. The majority of the participants reported they understood all the information, the amount was just right and that the information was useful. DISCUSSION: The KT tools were demonstrated to be acceptable and to improve knowledge. Future research is warranted to determine their impact on student experience during school vaccinations.
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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 | 0.048 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".