Social Contact Prior to COVID-19 and Longitudinal Mental Health Trajectories During COVID-19 Among Adults Ages ≥55
Bibliographic record
Abstract
Abstract Social support protects mental health during a crisis. We examined whether prior contact with social organizations and friends/neighbors was associated with better trajectories of loneliness, depression and self-rated memory during the COVID-19 pandemic. We conducted latent class analysis and regression analysis on longitudinal data from the COVID-19 Coping Study of US adults aged ≥55 from April-October 2020 (n=3105). Overall, prior contact with friends(B=-.075,p<.001), neighbors(B=-.048,p=.007), and social organizations(B=-.073,p<.001) predicted better mental health amid COVID-19. Three classes were identified: Class1 had the best outcomes, whereas Class3 had the worst outcomes and were most likely to live alone(B=.149,p<.001). For Class1, prior contact with social organizations(B=-.052,p=.044) predicted decreasing loneliness. For Class2, prior contact with friends(B=-.075,p<.001) predicted decreasing loneliness and better memory(B=-.130,p=.011). Conversely, prior contact with neighbors(B=-.165,p=.010) predicted worsening loneliness among Class3. Our findings pose new questions on the role of neighborhood networks to mitigate poor mental health outcomes among older adults during a crisis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".