The Medium-Term Psychosocial Impact of the 2021 Floods in Belgium: A Survey-Based Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: This study investigates the medium-term psychosocial impacts of the 2021 floods in Belgium, which caused fatalities and considerable infrastructural damage. Given similar events’ significant impacts on psychosocial well-being, this study seeks to answer three questions: whether there are medium-term (two years and further) effects on residents’ psychosocial well-being, whether demographic variables influence these effects, and how flood exposure impacts psychosocial well-being. Methods: We collected data in affected municipalities through an online survey, assessing demographic variables (e.g., age, gender, education, SES), flood exposure (e.g., being physically hurt, being faced with financial difficulties), and psychosocial well-being, employing two validated instruments for quantitative evaluation: the RAND-36 and the Traumatic Exposure Severity Scale (TESS). Results: The sample included 114 participants, with 54% reporting a deterioration in their psychosocial well-being after the floods. Additionally, over 50% mentioned the psychosocial impact of the floods. SES was the only significant demographic variable impacting psychosocial well-being, with lower SES linked to higher deterioration. Financial difficulties generated by the floods were the only considerable exposure factor. Furthermore, 22% discussed being unhappy with the organized response measures. Due to the sample size, confounding effects could not be checked. Conclusions: This study found a medium-term effect of the 2021 floods on psychosocial well-being, highlighting the need for policy adaptations focused on post-disaster psychosocial support. With lower SES and financial difficulties as risk factors, one needs to design policies tailored to these vulnerable groups. With climate change expected to increase flood events, context-specific policies are essential to boost resilience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it