Challenges facing Canadian Long-Term Care Homes and Retirement Homes during the COVID-19 pandemic
Bibliographic record
Abstract
ABSTRACT COVID-19 presented a crisis for long-term care homes (LTCHs) and retirement homes (RHs). This study explored the pandemic-related challenges LTCHs and RHs faced and the strategies they used to mitigate them. Ninety-one key informant interviews were conducted with LTCH and RH leadership across 47 homes (33 LTCHs, 14 RHs) in Ontario, Canada from February 2021 to July 2022. Findings confirmed evidence for three main challenges. First, leaders were challenged to implement infection prevention and control protocols and measures. Second, they needed supports to facilitate COVID-19 vaccine access and to promote vaccine confidence. Third, LTCH/RH staff experienced significant well-being challenges in the face of COVID-19 pressures. Findings also reveal a plethora of strategies implemented by homes, with ranging reports of perceived success. Homes’ needs evolved rapidly as the COVID-19 pandemic progressed. The use of a co-creation, responsive and tailored approach to address evolving barriers and meaningfully support homes during emergencies is recommended. Key points COVID-19 challenges in homes persisted over one year into the pandemic We describe the IPAC, vaccine and wellness challenges faced by LTCH and RH We used these data to design a congregate care home support program to navigate COVID-19 challenges
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".