MAKING “NONESSENTIAL” FAMILY/VOLUNTEER CAREGIVING ESSENTIAL IN LONG-TERM CARE HOMES
Bibliographic record
Abstract
Abstract For individuals living in long-term care (LTC), loneliness is often a concern. With the COVID-19 pandemic, this is only exacerbated as strict restrictions are put in place on visits between residents and their loved ones and on volunteer presence. Understanding how these changes affect residents, family caregivers, and volunteers is paramount to best implement changes with regards to how family/volunteer caregiving presence is managed during pandemics. The objective of this study was to gain a better understanding of the response to COVID-19 that pertains to the family caregiver and volunteer presence in LTC and increase the evidence about the impact of reduced levels of family caregivers and volunteers on residents, caregivers, and volunteers. A total of 64 semi-structured interviews were conducted with caregivers and volunteers. Of these interviews, 49 were one-time interviews and 15 were weekly interviews over a 5-month period to examine the impact of the ever-changing pandemic restrictions on caregivers, volunteers, and residents. Thematic analysis was used to analyze the interviews and two independent researchers coded each interview. Results highlight the importance of connections in LTC, the feeling that human rights were neglected, the importance of flexibility amongst staff, the role of caregivers as advocates for residents, increased caregiver guilt, and resident decline in physical and emotional well-being. The role of family caregivers and volunteers as essential in LTC homes will be discussed and recommendations to revisit policies on the family caregiver and volunteer presence to improve the preparedness for future pandemics and outbreaks will be presented.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".