MENTAL HEALTH AND WELL-BEING OF OLDER CARERS DURING THE COVID-19 PANDEMIC: EVIDENCE FROM ENGLAND
Bibliographic record
Abstract
Abstract Older people caring at home or in the community play a vital role in supporting population health and wellbeing and in protecting health and care systems, often at cost to their own health. Yet there has been very little research or policy attention given to this group of carers during the pandemic. Exploiting longitudinal data from Wave 9 (2018/19) and the first two COVID-19 sub-studies (June/July 2020; November/December 2020) of the English Longitudinal Study of Ageing, we use logistic and linear regression models to investigate associations between changes in provision of informal care and mental health during the pandemic, controlling for socio-demographic characteristics, pre-pandemic physical and mental health, and social isolation measures. During the first months of the pandemic, about a quarter of older people provided informal care (with ~10% caring for members living in the same household). Those caring in the household experience worse mental health during the pandemic. Even controlling for prior characteristics and lack of social interactions, those caring for family members in the household had higher odds of reporting elevated depressive symptoms (OR=1.67, 95%CI=1.07;2.62), poor self-rated health (OR=1.73, 95%CI=1.09;2.73), anxiety (OR=2.21, 95%CI=1.20;4.06) as well as lower quality of life (B=-0.85, 95%CI=-1.66;-0.05) and life satisfaction (B=-0.43; 95%CI=-0.78;-0.09) than those who were caring for friends and family outside the household. As we aim to build back society and restore the wellbeing of our populations, policies and services should be better directed to support those people who during the pandemic struggled to cope while caring for their family members.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".