Social support and psychosocial well-being among older adults in Europe during the COVID-19 pandemic: a cross-sectional study
Bibliographic record
Abstract
The objective of the study was to identify the association between social support and psychosocial well-being among men and women aged over 65 years in Europe during the COVID-19 pandemic. METHODS: Cross-sectional data on 36 621 men (n=15 719) and women (n=20,902) aged 65 years or higher were obtained from the ninth round of the Survey of Health, Ageing and Retirement in Europe. The outcomes were measured by psychosocial well-being reflected with self-reported depression, nervousness, loneliness and sleep disturbances. Social support was measured in terms of receiving help from own children, relatives and neighbours/friends/colleagues since the pandemic outbreak. RESULT: About one-third of the participants reported depression (31.03%), nervousness (32.85%), loneliness (32.23%) and sleep trouble (33.01%). The results of multivariable regression analysis revealed that social support was a protective factor to psychological well-being. For instance, receiving help from own children (RD=-0.13, 95% CI=-0.14 to -0.12), relatives (RD=-0.08, 95% CI=-0.11 to -0.06), neighbours/friends/colleagues (RD=-0.11, 95% CI=-0.13 to -0.09) and receiving home care (RD=-0.20, 95% CI=-0.22 to -0.18) showed significantly lower risk difference for depression. Similar findings were noted for loneliness, nervousness, and sleep trouble as well, with the risk difference being slightly different for men and women in the gender-stratified analysis. For instance, the risk difference in depression for receiving help from own children was -0.10 (95% CI=-0.12 to -0.08) among men compared with -0.12 (95% CI=-0.14 to -0.11) among women. The risk differences in the outcome measures were calculated using generalised linear model for binomial family. CONCLUSION: Findings of the present study highlight a protective role of social support on psychological well-being among both men and women. Developing strategies to promote social support, especially among older adults, may mitigate the rising burden of psychological illness during the COVID-19 pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".