COVID-19 and Long-Term Care: the Essential Role of Family Caregivers
Bibliographic record
Abstract
BACKGROUND: Those most at risk from severe COVID-19 infection are older adults; therefore, long-term care (LTC) facilities closed their doors to visitors and family caregivers (FCGs) during the initial wave of the COVID-19 pandemic. The most common chronic health condition among LTC residents is dementia, and persons living with dementia (PLWD) rely on FCGs to maintain their care provision. This study aims to evaluate the impact of visitor restrictions and resulting loss of FCGs providing in-person care to PLWD in LTC during the first wave of the COVID-19 pandemic. METHOD: An online survey and follow-up focus groups were conducted June to September 2020 (n=70). Mixed quantitative (descriptive statistics) and qualitative (thematic analysis) methods were used to evaluate study data. RESULTS: FCGs were unable to provide in-person care and while alternative communication methods were offered, they were not always effective. FCGs experienced negative outcomes including social isolation (66%), strain (63%), and reduced quality of life (57%). PLWD showed an increase in responsive behaviours (51%) and dementia progression. Consequently, 85% of FCGs indicated they are willing to undergo specialized training to maintain access to their PLWD. CONCLUSION: FCGs need continuous access to PLWD they care for in LTC to continue providing essential care.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".