Determining the Impact of COVID-19 on End-of-Life Experiences of Family Caregivers for People Living With Dementia
Bibliographic record
Abstract
Abstract COVID-19 has impacted all of our lives, but the population most at risk are older adults. Family caregivers (FCGs) for people living with dementia (PLWD) face challenges in providing care, which are compounded with the introduction of COVID-19 public health policies. The purpose of this study was to examine the experiences of FCGs where the PLWD died during the COVID-19 pandemic. FCGs were invited to participate in an online survey to examine their caregiving experiences during the COVID-19 pandemic, with the option of participating in a follow-up focus group. Sixteen FCGs whose family members with dementia died during the pandemic participated in the survey. A follow-up focus group was conducted to further examine how COVID-19 policies impacted their role as a caregiver in long-term care (LTC) and affected their ability to grieve. The results of the survey and focus group suggest that a lack of role clarity and inadequate communication channels between the FCG and LTC due to COVID-19 increased the strain FCGs faced during end-of-life care. At the end of life, public policies, such as reduced or no visitation, led to feelings of inadequacy and regret. Several participants also expressed appreciation for completing Advanced Care Planning documentation prior to COVID-19. Based on these results, policymakers can help ease the increased turmoil faced by FCGs during end-of-life care in future public health emergencies by involving FCGs of PLWD in the decision-making process. The completion of Advanced Care Planning documentation can also ease the burden FCGs may experience during end-of-life care.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".