The Impact of “Soft” and “Hard” Flood Adaptation Measures on Affected Population’s Mental Health: A Mixed Method Scoping Review
Bibliographic record
Abstract
BACKGROUND: The frequency and severity of floods has increased in different regions of the world due to climate change. It is important to examine how adaptation measures impact the mental health of individuals affected by these disasters. OBJECTIVE: The goal of this scoping review was to document the existing studies on the impact of flood adaptation measures in affected populations to identify the best preventive strategies and limitations that deserve further exploration. METHODS: This study followed the PRISMA-ScR guidelines. Inclusion criteria focused on studies in English or French available in MEDLINE and Web of Science that examined the impact of adaptation measures on the mental health of flood victims. Literature reviews or non-study records were excluded from the analysis. RESULTS: A total of 857 records were obtained from the examined databases. After 2 rounds of screening, 9 studies were included for full-text analysis. Six studies sought to identify the factors that drive resilience in flood victims, whereas 3 studies analyzed the impact of external interventions on their mental health. CONCLUSIONS: The limited number of studies demonstrates the need for public health policies to develop flood adaptation measures that can be used to support the mental health of flood victims.
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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 | 0.047 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".