Protecting Older Adult Residents in Care Facilities Against Influenza and COVID-19 Using the Influenza Communication, Advice and Reporting (FluCARE) App: Prospective Cohort Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Early detection and response to influenza and COVID-19 outbreaks in aged care facilities (ACFs) are critical to minimizing health impacts. The Sydney Local Health District (SLHD) Public Health Unit (PHU) has developed and implemented a novel web-based app with integrated functions for online line listings, detection algorithms, and automatic notifications to responders, to assist ACFs in outbreak response. The goal of the Influenza Outbreak Communication, Advice and Reporting (FluCARE) app is to reduce time delays to notifications, which we hope will reduce the spread, duration, and health impacts of an influenza or COVID-19 outbreak, as well as ease workload burdens on ACF staff. OBJECTIVE: The specific aims of the study were to (1) evaluate the acceptability and user satisfaction of the implementation and use of FluCARE in helping ACFs recognize, notify, and manage influenza and COVID-19 outbreaks in their facility; (2) identify the safety of FluCARE and any potential adverse outcomes of using the app; and (3) identify any perceived barriers or facilitators to the implementation and use of FluCARE from the ACF user perspective. METHODS: The FluCARE app was piloted from September 2019 to December 2020 in the SLHD. Associated implementation included promotion and engagement, user training, and operational policies. Participating ACF staff were invited to complete a posttraining survey. Staff were also invited to complete a postpilot evaluation survey that included the user Mobile Application Rating Scale (uMARS) measuring app acceptance, utility, and barriers and facilitators to use. An issues log was also prospectively maintained to assess safety. Survey data were analyzed descriptively or via content analysis where appropriate. RESULTS: Surveys were completed by 31 consenting users from 27 ACFs. FluCARE was rated 3.91 of 5 overall on the uMARS. Of the 31 users, 25 (80%) would definitely use FluCARE for future outbreaks, and all users agreed that the app was useful for identifying influenza and COVID-19 outbreaks at their facilities. There were no reported critical issues with incorrect or missed outbreak detection. User training, particularly online training modules, and technical support were identified as key facilitators to FluCARE use. CONCLUSIONS: FluCARE is an acceptable, useful, and safe app to assist ACF staff with early detection and response to influenza and COVID-19 outbreaks. This study supports feasibility for ongoing implementation and efficacy evaluation, followed by scale-up into other health districts in New South Wales.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".