Care burden, loneliness, and social isolation in caregivers of people with physical and brain health conditions in English‐speaking regions: Before and during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: Public health restrictions due to the COVID-19 (SARS CoV-2) pandemic have disproportionately affected informal caregivers of people living with long term health conditions. We aimed to explore levels of care burden, loneliness, and social isolation among caregivers of people with enduring physical and brain health conditions in English-speaking regions worldwide, by investigating outcomes before and during the COVID-19 pandemic. METHODS: A cross-sectional anonymous online survey data from 2287 English-speaking caregivers of people with long term health conditions from four English-speaking regions (UK, Ireland, USA, New Zealand) included measures of care burden, loneliness, and social isolation, reported before and during the COVID-19 pandemic. Analyses were descriptive, followed by an ordinal regression model for predictors of burden. RESULTS: Compared to pre-pandemic levels, all caregivers experienced a significant increase in burden, loneliness, and isolation. Caregivers of people with both brain health and physical conditions were the most burdened and had the highest levels of loneliness and isolation compared to caregivers of people with either a brain health or physical condition only. The increase in care burden among caregivers of people with brain health challenges was associated with caregiver's gender, moderate and severe emotional loneliness, magnitude and frequency of isolation during the pandemic, and care circumstances (cohabitation with the care recipient, restrictions on the ability to provide care). CONCLUSIONS: Health and social care interventions should target caregivers' care circumstances and psychological outcomes, particularly in women, accounting for the significant additional burden of care, loneliness, and isolation resulting from pandemic-related restrictions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".