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]
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".