An evaluation of barriers and facilitators to implementing multiplex rapid antigen testing for SARS-CoV-2 and influenza A and B in congregate living settings
Bibliographic record
Abstract
Introduction: Point of care multiplex rapid antigen testing (RAT) is a tool that can be used to mitigate and respond to facility-based infectious disease outbreaks. However, little is known about how to optimally implement this testing in congregate living settings (CLSs), including long term care homes (LTCHs), retirement homes (RHs), and shelters serving people experiencing homelessness. Our objective was to explore the barriers and facilitators to implementing a new device for multiplex RAT for COVID-19 and influenza across CLSs in the Greater Toronto Area, Canada. Materials and methods: Using key informant interviews, we assessed barriers and facilitators to implementing multiplex RAT across CLSs. Qualitative coding using the framework approach was used to identify themes. We used the Theoretical Domains Framework (TDF) and the Consolidated Framework for Implementation Research (CFIR) to identify individual and contextual-level barriers and facilitators to implementation. Identified barriers were then mapped to implementation strategies using theoretically-rooted frameworks and tools. Results: We completed 45 interviews with staff at CLSs (8 LTCHs, 4 RHs, 12 shelters) between January 2022 and March 2023. Four barriers to RAT implementation in CLSs emerged including: limited material resources for implementation; insufficient staff capacity to perform RAT testing; complexity of RAT implementation; and reluctance among staff to adopt a new testing process. Five facilitators to implementation were described including: training and implementation support for staff at the CLSs; site-level implementation champions; access to materials to support testing; perceived advantages of simultaneous testing for COVID-19 and influenza; and the usability and functionality of the RAT testing device. Twenty implementation strategies were identified through implementation strategy mapping. Discussion: Multiplex RAT options can empower CLS staff to promptly identify and respond to viral respiratory outbreaks. The use of evidence-based implementation strategies can enhance the effectiveness of using multiplex RAT to control outbreaks in CLSs.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".