The effect of a prosocial environment on health and well-being during the first COVID-19 lockdown and a year later
Bibliographic record
Abstract
The outset of the COVID-19 pandemic was characterized by prolonged periods of chronic stress and social isolation. While studies have investigated the changes to well-being (WB) during this period, the impact of the social environment on long-term physical and mental health requires further study. This study aimed to assess the factors influencing WB and health outcomes, with the hypothesis that a positive social environment would play a significant immediate and long-term role in improving WB and preventing the effects of anxiety associated with the pandemic. At time point 1 (April 2020), an Israeli sample of 206 participants (84% female, mean age 31.5) responded to traditional questionnaires assessing mental health and social support. Factors affecting WB were assessed within subjects during the first COVID-19 lockdown for 6 weeks using a daily survey (Beiwe phone application). A year later, in May 2021, at time point 2, the initial questionnaires were readministered to a subset of the same participants (N = 94). We found that anxiety during the first lockdown adversely affected WB and predicted health and WB deterioration a year later. In contrast, a high quality of social relationships was associated with better short- and long-term WB, and mitigated the adverse effects of anxiety. Daily activities, including physical activity, meditation, and romantic relations, were also positively associated with WB during the first lockdown but did not have long-term effects. In summary, our study underscores the enduring health advantages of a positive social environment, particularly during stressful periods. These results have implications for health policymakers: programs which support individuals with high anxiety and low support, by integrating them into community-based interventions, promise to enhance well-being (WB) and health, as well as to fortify the community as a whole.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".