Predictors of wellbeing during the COVID-19 pandemic: Key roles for gratitude and tragic optimism in a UK-based cohort.
Bibliographic record
Abstract
Here we examine the impact of the COVID-19 pandemic lockdown on wellbeing among UK-based respondents (N = 133). We explore the extent to which variables across wellbeing domains (physical activity, gratitude, tragic optimism, social support, and nature connection) contribute to wellbeing according to our previously proposed GENIAL model. Wellbeing was significantly reduced compared to both retrospective pre-lockdown measures (d=0.55) and a Scottish sample from 2018 (d=0.39). The regression model, containing wellbeing-related variables along with age, sex, and subjective socioeconomic status, accounted for up to 50% of the variance in wellbeing. While all predictor variables were significantly associated with wellbeing in zero-order correlations, only gratitude and tragic optimism contributed significantly to the regression model. These findings provide the first evidence for the contribution of these positive psychological factors to wellbeing during the COVID-19 lockdown. Implications for wellbeing at a time of great suffering and existential positive psychology (PP2.0) are discussed.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".