Using the CARD system for university-based pop-up vaccination clinics: A two-stage hybrid effectiveness-implementation study
Bibliographic record
Abstract
Mass vaccination clinics efficiently vaccinate large numbers of people. The CARD system (Comfort, Ask, Relax, Distract) is a vaccination delivery framework that can improve the experiences of vaccine recipients and providers. This two-stage hybrid effectiveness-implementation study implemented and sustained CARD in university-based vaccination clinics involving pharmacy student vaccinators. Stage 1 was a before-and-after study conducted across four COVID-19 vaccination clinics in November-December, 2022. Stage 2 was a single cohort study whereby CARD was sustained across four COVID-19/influenza vaccination clinics in November, 2023. In both stages, vaccine recipients rated experiences relative to last vaccination (primary outcome) and symptoms (pain, fear, dizziness) using surveys. Pharmacy student vaccinators completed attitudes and behaviors surveys; a subsample participated in focus groups. In Stage 1, more vaccine recipients in the after period (vs. before) reported a better vaccination experience relative to their last vaccination (64.0% vs 33.6%; p < .001; n = 181). Fear and pain were lower (1.1 vs. 1.7, p = .03; and 1.6 vs. 2.1, p = .02, respectively). In Stage 2, 57.4% (95% CI: 53.1%-61.6%; n = 542) of vaccine recipients reported a better experience. Across stages, pharmacy students had positive attitudes about acceptability, feasibility, and sustainability of CARD and reported high fidelity with CARD-recommended interventions. CARD is recommended for university-based vaccination clinics to improve vaccine recipient and provider experiences.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".