Resilience and Wellbeing Strategies for Pandemic Fatigue in Times of Covid-19
Bibliographic record
Abstract
The COVID-19 pandemic is truly one of the greatest collective health crises in history which have altered our life and living. For years, people have felt fatigued from following public health directives such as social distancing, wearing masks, washing hands frequently, and working or studying remotely without in-person interactions. In this paper, we explore strategies for resilience and wellbeing which can mitigate pandemic-caused stress and behavioural fatigue. We start with individual level strategies including reworking stress appraisals, the importance of psychological flexibility, reducing loneliness through adaptive online platform use, optimizing familial relationships when living in close quarters for a prolonged period, reducing symptoms of burnout and using adaptive distractions, using specific evidence-based resilience strategies. We discuss specific considerations which tap on our shared identities and shared responsibilities which can enhance a sense of community, especially for individuals from marginalized backgrounds and how suicide risks can be minimized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".