#Morethanavisitor: Experiences of <scp>COVID</scp>‐19 visitor restrictions in Canadian long‐term care facilities
Bibliographic record
Abstract
Objective: The purpose of this study was to understand the experiences of families, residents, and staff around visitor restriction policies in long-term care during the COVID-19 pandemic in Canada. Background: Beginning in March 2020, public health orders across Canada restricted visitors to long-term care facilities to curb the spread of the infection. This included family caregivers who provide significant support to residents to meet their physical, psychological, social, and safety needs. Method: We collected data from publicly available news and social media. News articles, blogs, and tweets from Canada were collected from March 2020 to April 2021. In total, 40 news articles, eight blogs, and 23 tweets were analyzed using generic qualitative description. Results: Reports from family members indicate that some residents may have died from malnutrition, dehydration, and isolation, rather than from COVID-19, because of the sudden and prolonged absence of family caregivers. There are long-term impacts on family suffering and long-term care worker burnout. Policy and structural issues were identified. Conclusion: Experiences in long-term care reflected not only impacts of pandemic-related visitor restrictions, but also long-standing funding and workforce issues. Implications: Involvement of family, and specifically family caregivers, is crucial in policy decisions, even in unusual circumstances, such as the pandemic.
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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 | 0.014 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".