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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».