Social and Mental Health Impact of Nuclear Disaster in Survivors: A Narrative Review
Bibliographic record
Abstract
This narrative review synthesizes the literature on the psychological consequences of the Three Mile Island nuclear accident of 1979, the Chernobyl nuclear disaster of 1986, and the Fukushima nuclear disaster of 2011. A search was conducted on OVID for studies in English from 1966 to 2020. Fifty-nine studies were included. Living through a nuclear disaster is associated with higher levels of PTSD, depression, and anxiety. Decontamination workers, those living in closest proximity to the reactor, and evacuees experience higher rates of mental health problems after a nuclear disaster. Those with greater psychological resilience and social supports experience lower rates of psychological distress. Individual-level interventions, such as mindfulness training, behavioral activation, and cognitive reappraisal training, have shown modest benefits on improving psychological wellbeing. At the population level, many of the measures in place aimed at reducing exposure to radiation actually increase individuals' anxiety. Technology-based supports have been studied in other types of natural disasters and it may be beneficial to look at mobile-based interventions for future nuclear disasters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".