Exploring experiences of loneliness among Canadian long‐term care residents during the COVID‐19 pandemic: A qualitative study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has significant impact on long-term care (LTC) residents' health and well-being. OBJECTIVES: This study investigated resident experiences of loneliness during the COVID-19 pandemic in Canadian LTC homes to offer lessons learned and implications. METHODS: 15 residents and 16 staff members were recruited from two large urban Canadian LTC homes with large outbreaks and fatalities. We used a telepresence robot to conduct one-on-one semi-structured interviews with participants remotely. We applied the Collaborative Action Research (CAR) methodology and report the early phase of CAR focused on collecting data and reporting findings to inform actions for change. Thematic analysis was performed to identify themes. RESULTS: Four themes were identified. The first two themes characterise what commonly generated feelings of loneliness amongst residents, including (1) social isolation and missing their family and friends and (2) feeling hopeless and grieving for lives lost. The second two themes describe what helped residents alleviate loneliness, including (3) social support and (4) creating opportunities for recreation and promoting positivity. CONCLUSIONS: Residents living in LTC experienced significant social isolation and grief during the pandemic that resulted in loneliness and other negative health consequences. IMPLICATIONS FOR PRACTICE: Promoting meaningful connection, safe recreational activities and a positive atmosphere in LTC homes during the pandemic may help mitigate residents' experiences of loneliness due to social isolation and/or grief and enhance their quality of life.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".