Psychosocial impact of the summer 2007 floods in England
Bibliographic record
Abstract
BACKGROUND: The summer of 2007 was the wettest in the UK since records began in 1914 and resulted in severe flooding in several regions. We carried out a health impact assessment using population-based surveys to assess the prevalence of and risk factors for the psychosocial consequences of this flooding in the United Kingdom. METHODS: Surveys were conducted in two regions using postal, online, telephone questionnaires and face-to-face interviews. Exposure variables included the presence of flood water in the home, evacuation and disruption to essential services (incident management variables), perceived impact of the floods on finances, house values and perceived health concerns. Validated tools were used to assess psychosocial outcome (mental health symptoms): psychological distress (GHQ-12), anxiety (GAD-7), depression (PHQ-9) and probable post-traumatic stress disorder (PTSD checklist-shortform). Multivariable logistic regression was used to describe the association between water level in the home, psychological exposure variables and incident management variables, and each mental health symptom, adjusted for age, sex, presence of an existing medical condition, employment status, area and data collection method. RESULTS: The prevalence of all mental health symptoms was two to five-fold higher among individuals affected by flood water in the home. People who perceived negative impact on finances were more likely to report psychological distress (OR 2.5, 1.8-3.4), probable anxiety (OR 1.8, 1.3-2.7) probable depression (OR 2.0, 1.3-2.9) and probable PTSD (OR 3.2, 2.0-5.2). Disruption to essential services increased adverse psychological outcomes by two to three-fold. Evacuation was associated with some increase in psychological distress but not significantly for the other three measures. CONCLUSION: The psychosocial and mental health impact of flooding is a growing public health concern and improved strategies for minimising disruption to essential services and financial worries need to be built in to emergency preparedness and response systems. Public Health Agencies should address the underlying predictors of adverse psychosocial and mental health when providing information and advice to people who are or are likely to be affected by flooding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".