The Mediating Role of Neighborhood Networks on Long-Term Trajectories of Subjective Well-Being After Covid-19
Bibliographic record
Abstract
We investigate the trajectories of people's subjective well-being, measured as their overall life satisfaction at five points in time before, during, and after Covid-19 in Switzerland. Using sequence analysis and hierarchical clustering, we identify three groups of typical trajectories. About half of all respondents experienced a decline in well-being right after the first lockdown and subsequent recovery to high, pre-pandemic levels. A quarter consistently reports very high satisfaction throughout all five waves, and another quarter experienced declining well-being since the outbreak of the pandemic. As a second contribution, we then demonstrate how improving relations with neighbors increases the likelihood of recovering from the negative impact of the pandemic on subjective well-being. This effect is largely constant across social groups. Conceptualizing vulnerability as the extent to which social groups with different endowments (e.g., financial situation or individual social networks) cope differently with (exogenous) stressors, we further find slightly more pronounced positive effects of improving neighborly relations during the pandemic for more vulnerable people in terms of household finances and education. Moreover, being able to count on emotional support from neighbors and friends prior to the pandemic generally guarded against experiencing declining well-being. Meanwhile, people with less financial means, poorer health, and less support from friends and neighbors are also more likely to be in the trajectory cluster of declining well-being.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".