COVID-19 vaccination delivery in long-term-care using the CARD (Comfort Ask Relax Distract) system: Mixed methods study of implementation drivers
Bibliographic record
Abstract
CARD (comfort, ask, relax, distract) is a vaccine delivery framework that includes interventions to improve the patient’s experience. CARD has not been previously implemented in long-term care (LTC) settings. This study evaluated drivers to implementation for COVID-19 vaccinations in an LTC facility. Postimplementation interpretive evaluation including qualitative interviews and quantitative surveys with eight participants. The Consolidated Framework for Implementation Research (CFIR) was used for analysis. Adverse reactions to vaccinations and CARD interventions, including local reactogenicity and systemic reactions, were abstracted from medical charts of residents. Eight CFIR constructs emerged. Staff perceived CARD was complex because it added steps to vaccination delivery. Motivated to meet residents’ needs, a receptive implementation climate of support among staff led to using strategies within CARD, such as administering topical anesthetics and omitting alcohol skin antisepsis prior to injections. Having an effective network like the residents council positively influenced implementation by allowing residents to voice their opinions. Facilitators to implementation included staff knowledge and beliefs and staff’s commitment to their organization, which was focused on person-centered care. Barriers included lack of available resources (inadequate staffing), insufficient communication between management and staff and lack of awareness of CARD, and external policies not aligned with CARD. Chart reviews conducted for 93 vaccinated residents corroborated perceptions of vaccination and CARD intervention safety, revealing a low rate of local and systemic adverse reactions and no cases of skin infection. We identified positive and negative implementation drivers. Future research is recommended to expand the strategies employed and involve residents more directly.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".