Does It Matter Who You Live with during COVID-19 Lockdown? Association of Living Arrangements with Psychosocial Health, Life Satisfaction, and Quality of Life: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Living arrangements might greatly impact psychosocial health and quality of life, particularly during the COVID-19 lockdown. This pilot study aimed to examine the association of different common living arrangements with psychosocial health, life satisfaction, and quality of life among Chinese adults during the COVID-19 lockdown. METHODS: An anonymous online survey was conducted using convenience sampling through the WeChat application in February 2020. Mental health (Patient Health Questionnaire-2, Generalized Anxiety Disorder-2, post-traumatic stress disorder symptoms, Patient Health Questionnaire-15, and meaning in life), social health (UCLA-3), quality of life (EQ5D and EQ-VAS), and life satisfaction were measured. Linear regression models were used. RESULT: The study included 1245 adults (mean age: 34.14 ± 10.71) in China. Compared to other living arrangements, participants who "live with partner and children" or "live with partner, children and parents" were more likely to have better outcomes of mental health, social health, quality of life, and life satisfaction. Participants who "live with parents or grandparents" or "live with partner" were more likely to have better health outcomes compared with those who "live with children" or "live alone". CONCLUSION: Living with a partner, children, and/or parents could be a protective factor against poor psychosocial health during lockdown and quarantine.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".