Exploring the Impact of COVID-19 on Family Caregivers of Persons Living With Dementia in the Community
Bibliographic record
Abstract
Abstract COVID-19 public health measures have significantly impacted persons living with dementia (PLWD) and family caregivers (FCGs). Given the restrictions on in-person services, many PLWD were not able to access their usual supports and activities, resulting in FCGs stepping in to support exercise, leisure, socialization, spirituality, and activities of daily living. At the same time, FCGs’ own support networks were significantly reduced or no longer available. We conducted in-depth qualitative interviews with 20 FCGs of PLWD in the community to explore the impact of COVID-19 on their well-being. Data were analyzed using thematic analysis. Caregiving during COVID-19 was described as ‘draining’ and ‘stressful’, with the support needs of PLWD increasing at a time when fewer supports were available. Reaching out to others, using technology, and setting boundaries were strategies FCGs used to cope. Despite the considerable impacts of COVID-19, FCGs of PLWD demonstrated their resilience in supporting themselves and their PLWD.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".