A systematic review of individual, social, and societal resilience factors in response to societal challenges and crises
Bibliographic record
Abstract
Societal challenges put public mental health at risk and result in a growing interest in resilience as trajectories of good mental health during stressor exposure. Resilience factors represent multilevel psychosocial resources that increase the likelihood of resilient responses. This preregistered systematic review aims at summarizing evidence on the predictive value of individual, social and societal resilience factors for resilient responses to societal challenges and crises. Eligible studies examined the predictive value of resilience factors in stressor-exposed populations in high-income countries by means of multinomial regression models based on growth mixture modeling. Five databases were searched until August 2, 2023. Data synthesis employed a rating scheme to assess the incremental predictive value of resilience factors beyond sociodemographic variables and other resilience factors. An adapted version of the Newcastle-Ottawa Scale was used for risk of bias assessment. Fifty studies (sample sizes: 360-65,818 participants) with moderate study quality reported on various stressors (e.g., pandemics, natural disasters, terrorist attacks). Higher income, socioeconomic status and perceived social support, better emotion regulation and psychological flexibility were related to more resilient responses. The association between resilience factors and resilient responses was stronger in samples with younger mean age and a larger proportion of women. Most studies used non-representative convenience samples and effects were smaller when accounting for sociodemographic variables and other resilience factors. For many factors, findings were mixed, supporting the importance of the fit between resilience factors and situational demands. Research into social and societal resilience factors and multilevel resilience interventions is needed. Preregistration-ID: 10.17605/OSF.IO/GWJVA. Funding source: Robert Koch Institute (ID: LIR_2023_01).
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".