COVID-19 AND THE CARE OF ASSISTED LIVING RESIDENTS: THE COVCARES PROGRAM OF RESEARCH
Bibliographic record
Abstract
Abstract This presentation describes the goals, design and methods of the COVID-19 and the Care of Assisted living Residents (COVCARES) program of research. Our goals were to address critical knowledge gaps related to how COVID-19 impacted assisted living (AL) residents, family/friend caregivers, and homes, and to compare how outcomes differed between AL and nursing homes (NH). Partnering closely with provincial Alzheimer Societies, family caregiver advocates, and health policy decision makers, we conducted two interconnected studies: The first was a prospective cohort study, surveying family/friend caregivers of AL residents and directors of care of AL homes in the Canadian provinces of Alberta and British Columbia twice (10/2020–04/2021 and 07/2021–09/2021). 673 caregivers and 104 AL homes responded to our first survey. Of those, 386 caregivers and 78 AL homes submitted a second survey. The second was a population-based retrospective cohort study (01/2017–12/2021), using clinical and health administrative data of all AL and NH residents in Alberta. Data included Resident Assessment Instrument (RAI) records, acute care and emergency department admission data, prescription medications, data on COVID-19 infections, and vital statistics of 23,355 AL residents and 41,583 NH residents. Analyzing each of the aforementioned data sets individually, and linking family and facility surveys, as well as resident and facility survey data, we provide unique insights into the impacts of the COVID-19 pandemic on AL settings. The presentations in this symposium will share selected key findings from our program of research.
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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 | 0.054 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".