THE IMPACT OF COVID-19 ON RESIDENTS AND FAMILY/FRIEND CAREGIVERS IN ASSISTED LIVING HOMES
Bibliographic record
Abstract
Abstract Healthcare reforms have neglected assisted living (AL) and nursing homes (NHs) for decades, setting them up for the excessive rates of death and suffering during COVID-19. Research before and during the pandemic has primarily focused on NHs, largely overlooking AL. AL is rapidly expanding, caring for people with similar vulnerabilities as those in NHs, yet is less regulated, offers fewer services, has lower staffing/skill mix levels and requires significant family/friend involvement in care. This symposium presents a program of research (COVCARES, COVID-19 and the Care of Assisted living Residents), aiming to understand how the pandemic has impacted AL resident, family/friend, and facility outcomes, and how resident outcomes compare between NHs and AL. Our research started over a decade ago with the first population-based cohort study comparing AL and NHs in Canada. Our current research includes repeated surveys (10/2020–04/2021 and 07/2021–09/2021) with family/friend caregivers and AL facilities, and population-based clinical and health administrative data (2017-2021) from AL and NH residents in Western Canada. Five presentations will report on the design/methods/goals of COVCARES (#1), the impact of the pandemic on family/friend involvement in AL resident care (#2), impacted of the pandemic on psychotropic drug prescriptions in AL (#3), and the association of AL home preparedness for and response to the pandemic on resident pain (#4) and loneliness (#5). Our discussant (Anna Beeber) will highlight similarities and differences between AL and NHs, similarities and differences in both settings between the US and Canada, and how policymakers can account for these differences.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".