Wellness Hub: Understanding COVID-19 Transmission through Implementing and Evaluating an Intervention to Support Wellness, Infection Prevention and Control, Vaccine Uptake, and Other Wraparound Care Needs in Long-term Care and Retirement Homes [WH, study data contributed to the CITF Databank]
Notice bibliographique
Résumé
Background: Long-term care home (LTCH) residents are more vulnerable to COVID-19 due to older age, weakened immune systems, and other predisposed health conditions, and LTCH staff are at a higher risk of COVID-19 infection than the rest of the community. It is important to understand immunity and vaccine response in LTCH residents and staff to protect against serious outbreaks in the future. Aims of the CITF Funded study: The study aimed to 1) determine the seroprevalence and risk factors of infection by SARS-Cov-2 among LTCH residents, staff, and essential caregivers; 2) implement infection prevention and control (IPAC) and monitor infection rates by saliva samples, and 3) explore barriers to implementing dried blood spot (DBS) and saliva testing in long term care home and retirement home residents, staff, and their family members. Methods: This cross-sectional study enrolled residents and their family members, as well as staff and their household members across 72 selected long-term care and retirement homes in Ontario, Canada. All participants completed a questionnaire and provided a DBS sample at baseline and at a follow-up 9 months later. Staff, staff household members, and families/caregivers of residents were placed into “high-risk exposure” or “low-risk exposure” groups and provided a saliva sample for PCR testing. Some staff participants were entered into a nested case study where they were followed up weekly for PCR testing and optional symptomatic saliva testing through the Wellness Hub. Contributed dataset contents: TThe Wellness Hub datasets include 1616 participants who completed baseline questionnaires between May 2021 and June 2023. Over 99% of participants gave one or more dried blood spot samples for SARS-CoV-2 serology between December 2020 and June 2023. Six additional participants gave dried blood spots or blood samples for SARS-CoV-2 serology without completing a questionnaire. Questionnaire variables include data in the following areas of information: demographics (age, sex and gender, race-ethnicity and indigeneity, province, education, household composition, occupations), flu vaccination behaviour , longitudinal follow-up for COVID infection (dates of positive tests, hospitalizations), exposure risks, SARS-CoV-2 vaccination. The Objective 2 datasets include 141 participants who completed baseline questionnaires between February and March in 2021. 98% of (almost all, except three,) participants gave one or more blood samples or serum for SARS-CoV-2 serology between February 2021 and April 2023. Twelve additional participants gave blood samples or serum for SARS-CoV-2 serology without completing a questionnaire. Questionnaire variables include data in the following areas of information: demographics (age, sex and gender, province), general health (height and weight; chronic conditions; flu vaccine), longitudinal follow-up for COVID infection (dates of positive tests, symptoms), SARS-CoV-2 vaccination.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,006 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».