A meta-review of psychological resilience during COVID-19
Bibliographic record
Abstract
Psychological resilience has emerged as a key factor in mental health during the global COVID-19 pandemic. However, no work to date has synthesised findings across review work or assessed the reliability of findings based on review work quality, so as to inform public health policy. We thus conducted a meta-review on all types of review work from the start of the pandemic (January 2020) until the last search date (June 2021). Of an initial 281 papers, 30 were included for review characteristic reporting and 15 were of sufficient review quality for further inclusion in strategy analyses. High-level strategies were identified at the individual, community, organisational, and governmental levels. Several specific training and/or intervention programmes were also identified. However, the quality of findings was insufficient for drawing conclusions. A major gap between measuring the psychological resilience of populations and evaluating the effectiveness of strategies for those populations was revealed. More empirical work, especially randomised controlled trials with diverse populations and rigorous analyses, is strongly recommended for future research.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".