Associations of Social Support With Physical and Mental Health Symptom Burden After COVID-19 Hospitalization Among Older Adults
Bibliographic record
Abstract
BACKGROUND: Despite significant support system disruptions during the coronavirus 2019 (COVID-19) pandemic, little is known about the relationship between social support and symptom burden among older adults following COVID-19 hospitalization. METHODS: From a prospective cohort of 341 community-living persons aged ≥60 years hospitalized with COVID-19 between June 2020 and June 2021 who underwent follow-up at 1, 3, and 6 months after discharge, we identified 311 participants with ≥1 follow-up assessment. Social support prehospitalization was ascertained using a 5-item version of the Medical Outcomes Study Social Support Survey (range, 5-25), with low social support defined as a score ≤15. At hospitalization and each follow-up assessment, 14 physical symptoms were assessed using a modified Edmonton Symptom Assessment System inclusive of COVID-19-relevant symptoms. Mental health symptoms were assessed using Patient Health Questionnaire-4. Longitudinal associations between social support and physical and mental health symptoms, respectively, were evaluated through multivariable regression. RESULTS: Participants' mean age was 71.3 years (standard deviation, 8.5), 52.4% were female, and 34.2% were of Black race or Hispanic ethnicity. 11.8% reported low social support. Over the 6-month follow-up period, low social support was independently associated with higher burden of physical symptoms (adjusted rate ratio [aRR], 1.26; 95% confidence interval [CI], 1.05-1.52), but not mental health symptoms (aRR, 1.14; 95% CI, 0.85-1.53). CONCLUSIONS: Low social support is associated with greater physical, but not mental health, symptom burden among older survivors of COVID-19 hospitalization. Our findings suggest a potential need for social support screening and interventions to improve post-COVID-19 symptom management in this vulnerable group.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".