Qualitative study of frontotemporal lobar degeneration caregivers' coping strategies and concerns during COVID‐19
Bibliographic record
Abstract
Abstract Background The coronavirus (COVID‐19) pandemic has been particularly arduous on those living with dementia and their caregivers as they depend on in‐person support and programs. High rates of depression and anxiety were reported in Alzheimer's disease during the COVID‐19 pandemic (El Haj, 2020). In patients with frontotemporal lobar degeneration (FTLD) there are additional challenges as these patients have motor, language, behavior disorders and cognitive deficits. We examined the impact of COVID‐19 on FTLD caregivers. Method From March 2020‐June 2020, we carried out a qualitative study with FTLD caregivers to determine the impact of COVID‐19 on their continued ability to care for the person with dementia. We examined the following: patient’s age, relationship to primary caregiver, number of people in household, access to virtual telecommunication, access to exercise, caregivers’ greatest supports, their identified needs and fears. Result We included 52 FTLD patient‐caregiver dyads (5 behavioral variant (bvFTD), 17 primary progressive aphasia (PPA), 19 Corticobasal Syndrome (CBS), and 11 Progressive Supranuclear Palsy (PSP)). Mean age of patients was 67.9+/‐8.76. 57% of caregivers were female. 60% of patients lived alone with spouse, 23% lived with spouse and a child, 2 patients lived in LTC, 3 with a family member (not spouse) or friend, 1 was in hospital, and 2 lived alone. 75% had access to virtual telecommunication. 44% were engaged in physical activity (mostly walking). 16 caregivers (25%) reported their greatest needs were reinstating or increasing Personal Support Worker hours, 8 (15%) needed access to other health care providers and for 7 (13%) they wanted Adult Day Programs to reopen. 31 caregivers (60%) reported their greatest support was family; 8 (15%) were grateful for community supports. 22 caregivers (42%) were concerned about ongoing decline whilst only 11 (21%) feared COVID. Conclusion We provide some qualitative data on FTLD caregivers’ experience during COVID. The most frequent concern was ongoing decline. Over 50% of caregivers reported their greatest need was access to services and their greatest support was family network. Enabling easily accessible and safe in‐home care is important to support people living at home with FTLD during the pandemic. REFERENCES: El Haj, M. Psychiatry Research, (2020).
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".