Resilience and Adaptivity Were Strong Correlates of Wellbeing in the Early Stages of the Covid-19 Pandemic
Bibliographic record
Abstract
Across the globe, COVID-19 continues to disrupt everyday lives, with serious consequences for individuals' health and wellbeing. This retrospective, multinational survey study draws upon the Personal Resource Allocation (PRA) framework to explore how various demographic factors, individual differences, and leadership determine the perceived and actual impact of COVID-19 on health (mental, physical) and wellbeing (work, home, general) across five countries: Canada, France, Germany, the United Kingdom, and the United States. Having dependents under 12, working more hours since the onset, and having essential worker status led to better wellbeing outcomes. All three individual differences (adaptivity, resilience, remote work training) were positively related to engagement, with resilience and remote work also relating to better health for these individuals. Lastly, perceptions of COVID impact on mental and physical health had negative consequences for general wellbeing, while effective leadership perceptions predicted work engagement. No differences were found between the five countries. Findings highlight the importance of personal resources in determining the pandemic’s impact on wellbeing.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".