Development and refinement of an online CARD (Comfort Ask Relax Distract) course for organizations and providers delivering vaccinations and other needle procedures
Bibliographic record
Abstract
The CARD system (Comfort-Ask-Relax-Distract) is an evidence-based protocol for mitigating vaccine injection-related reactions, such as pain, fear and fainting. We developed a new online CARD training course to educate organizations and providers about CARD and evaluated usability and impact on conceptual knowledge and attitudes. After an initial development phase, three iterative phases of user testing were conducted in individuals with different levels of familiarity with CARD. In the first two phases, experts and non-experts provided feedback using surveys and semi-structured interviews. In the third phase, new learners answered a knowledge test and attitudes survey. Revisions occurred throughout based on qualitative and quantitative analyses, guided by the Universal Design for Learning (UDL) framework. Thirty-one healthcare providers and trainees from 8 disciplines participated across study phases. They held positive attitudes in all components of the UDL, including engagement (e.g. perceptions of relevance and value), representation (e.g. multiple ways included to perceive information, common language), and action and expression (e.g. access to materials, interactivity). Modifications addressed feedback regarding content development, organization and expression of information, and course navigation. Phase 3 participants demonstrated knowledge and positive attitudes about CARD, including confidence and intention to use CARD. In summary, this study developed and refined an online CARD course to educate organizations and providers about CARD in a feasible way and to improve readiness for implementation. The course has the potential to improve efficiency and effectiveness of CARD implementation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".