Health Equity Implications of the COVID-19 Lockdown and Visitation Strategies in Long-Term Care Homes in Ontario: A Mixed Method Study
Bibliographic record
Abstract
The COVID-19 pandemic has negatively impacted the lives and well-being of long-term care home residents. This mixed-method study examined the health equity implications of the COVID-19 lockdown and visitation strategies in long-term care homes in Ontario. We recruited long-term care home residents, their family members and designated caregivers, as well as healthcare workers from 235 homes in Ontario, Canada. We used online surveys and virtual interviews to assess the priority, feasibility, and acceptability of visitation strategies, and to explore the lived experiences of participants under the lockdown and thereafter. A total of n = 201 participants completed a survey and a purposive sample of n = 15 long-term care home residents and their family members completed an interview. The initial lockdown deteriorated residents’ physical, mental, and cognitive well-being, and disrupted family and community ties. Transitional visitation strategies, such as virtual visits, were criticised for lack of emotional value and limited feasibility. Designated caregiver programs emerged as a prioritised and highly acceptable strategy, one that residents and family members demanded continuous and unconditional access to. Our findings suggest a series of equity implications that highlight a person-centred approach to visitation strategies and promote emotional connection between residents and their loved ones.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".