Impact of Loneliness and Social Isolation on Mental Health Outcomes Among Individuals With Rheumatic Diseases During the <scp>COVID</scp>‐19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: The study objective was to assess mental and social health outcomes for individuals with rheumatic disease during the COVID-19 pandemic and evaluate the relationship of loneliness and social isolation with depression and anxiety. METHODS: We administered an international cross-sectional online survey to individuals with rheumatic disease(s) (≥18 years) between April 2020 and September 2020, with a follow-up survey from December 2020 to February 2021. We used questionnaires to evaluate loneliness (3-item UCLA Loneliness Scale [UCLA-3]), social isolation (Lubben Social Network Scale [LSNS-6]), depression (Patient Health Questionnaire [PHQ-9]), and anxiety (Generalized Anxiety Disorder 7-item [GAD-7] Scale). We used multivariable linear regression models to evaluate the cross-sectional associations of loneliness and social isolation with depression and anxiety at baseline. RESULTS: Seven hundred eighteen individuals (91.4% women, mean age: 45.4 ± 14.2 years) participated in the baseline survey, and 344 completed the follow-up survey. Overall, 51.1% of participants experienced loneliness (UCLA-3 score ≥6) and 30.3% experienced social isolation (LSNS-6 score <12) at baseline. Depression (PHQ-9 score ≥10) and anxiety (GAD-7 score ≥10) were experienced by 42.8% and 34.0% of participants at baseline, respectively. Multivariable models showed that experiencing both loneliness and social isolation, in comparison to experiencing neither, was significantly associated with an average 7.27 higher depression score (ß = 7.27; 95% confidence interval [CI]: 6.08-8.47) and 5.14 higher anxiety score (ß = 5.14; 95% CI: 4.00-6.28). CONCLUSION: Aside from showing substantial experience of loneliness and social isolation during the COVID-19 pandemic, our survey showed significant associations with depression and anxiety. Patient supports to address social health have potential implications for also supporting mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".