WELL-BEING DURING THE COVID-19 PANDEMIC: THE ROLES OF DEMOGRAPHICS, PERSONALITY, AND SOCIAL TIES
Bibliographic record
Abstract
Abstract The COVID-19 pandemic continues to exert widespread impacts on individuals, particularly older adults (Tyrrell & Williams, 2020). This symposium capitalizes on a variety of data sources to advance our understandings of the psychosocial impact of the pandemic on older adults. The first two papers consider the importance of personality characteristics in understanding the effects of social distancing. Fiori et al. highlight the potential for sociability to act as a liability during times of social distancing, finding that sociability exacerbated the effects of social distancing on mental health outcomes in a sample of community-dwelling older adults. Ryan’s paper focuses on the Big Five Personality traits, age, and population density as key characteristics explaining differences in subjective well-being during the pandemic. Next, Van Vleet et al. apply a mixed-methods approach to investigate when older adults expect life to go back to normal, finding that expectations about the future became more positive with the passage of time. The final two papers consider the importance of adults’ home social context during the pandemic. Newton examines relationships between living alone and well-being outcomes among older Canadian women, finding that perceived COVID-19 impact was significant only at T1 and living alone was linked to poorer well-being by T2. Birditt et al. examine how individuals’ and partner’s COVID-19 stress and couples’ racial composition are related to affective experiences measured via ecological momentary assessments, finding that husbands’ stress impacted both partners’ well-being, and that associations differed by race. Sherman will lead a discussion to synthesize these new findings.
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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 | 0.003 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".