The Role of Social Determinants in Mental Health and Resilience After Disasters: Implications for Public Health Policy and Practice
Bibliographic record
Abstract
In this general literature review, we will explore the impacts and contribution of social determinants to mental health and resiliency following both natural and man-made disasters. Natural disasters, such as wildfires, earthquakes, tsunamis, and hurricanes, as well as man-made disasters, such as civil wars, have been known to inflict significant damage to the mental health of the victims. In this paper, we mainly explore some most studied vulnerability and protective social determinant factors such as gender, age, ethnicity, socials support and socioeconomic status for the mental health and resiliency in survivors of such disasters. Several other possible factors such as previous trauma, childhood abuse, family psychiatric history, and subsequent life stress that were explored by some studies were also discussed. We conducted a literature search in major scientific databases, using keywords such as: mental health, social determinants, disasters, wildfires, earthquakes, terrorist attacks, and resilience. We discuss the implications for public health policy and practice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".