“We’ve all Lost so Much”: The Experiences of Essential Family Caregivers’ Long Term Care Visitations During COVID-19
Bibliographic record
Abstract
Abstract Family caregivers are integral to the care of long-term care (LTC) residents. COVID-19 public health policies initially restricted all essential caregivers from visiting LTC homes. In lieu of in-person visitations, caregivers were allowed technology-based visits then restrictive outdoor visits, followed by indoor visitations. This study aims to illuminate the experiences of essential caregivers’ as they visited their loved ones in LTC during COVID-19’s restrictive policies. We conducted seven caregiver focus groups (N=30) from Ontario and British Columbia, Canada. Thematic analysis and line-by-line coding were completed using NVivo. We found six themes that were common to all the visitation types: 1) “LTC Home disorganization” to facilitate visits and poor communication; 2) “Lack of staffing and resources”; 3) “Mistreatment from staff and management” as caregivers were seen as inconveniences; 4) “Shock and disbelief” when family members first saw their loved ones; 5) “Significant lack of person-centered or family-centered ethos” for example the residents’ needs were ignored such that their cognitive and physical impairments sometimes made visitations impossible, as well as the burden of multiple weekly COVID-19 tests; and, 6) “Collateral damage” in the form of trauma and irreparable harm to the relationships between residents and families. These results emphasized caregivers who ultimately felt betrayed and ignored by the broader healthcare system. Our findings provide an in-depth understanding of how COVID-19 public health policies have impacted the essential caregivers and the long-lasting impacts on residents and caregivers alike. Understanding caregiver’s experiences can inform future pandemic response policies and encourage more person-centered protocols.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".