“Picturing a Way Forward”: Strategies to Manage the Effects of COVID-19-Related Isolation on Long-Term Care Residents and Their Informal Caregivers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Strategies to manage the coronavirus disease 2019 (COVID-19) pandemic included widespread use of physical distancing measures. These well-intended strategies adversely affected long-term care (LTC) residents' socialization and their caregiving arrangements, leading to exacerbation of social isolation and emotional distress for both residents and their caregivers. This study aimed to understand how these measures affected informal caregivers of people living in LTC homes in Ontario. Strategies to increase socialization and promote social connection during and post-COVID-19 were also explored. RESEARCH DESIGN AND METHODS: This qualitative study used descriptive and photovoice approaches. Of the 9 potential caregivers identified, 6 participated in the study and shared their experiences and photographic reflections in virtual focus group sessions. RESULTS: Findings highlighted the increased social isolation experienced by people living in LTC and their caregivers during COVID-19. Caregivers reported pronounced declines in residents' well-being and were frustrated by challenges connecting with their family members during quarantine. Attempts made by LTC homes to maintain social connections, such as window visits and video calls, did not fulfill the social needs of residents and their caregivers. DISCUSSION AND IMPLICATIONS: Findings underscore a need for better social support and resources for both LTC residents and their caregivers going forward to prevent further isolation and disengagement. Even in times of lockdown, LTC homes must implement policies, services, and programs that promote meaningful engagement for older adults and their families.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".