Perceptions of Mental Health and Wellbeing Following Residential Displacement and Damage from the 2018 St. John River Flood
Bibliographic record
Abstract
Climate change has spurred an increase in the prevalence and severity of natural disasters. Damage from natural disasters can lead to residential instability, which negatively impacts mental health and wellbeing. However, research on the mental health of residents who are displaced after natural disasters is relatively novel and needs more study. This study investigates experiences of mental health in residents in New Brunswick, Canada, who experienced residential damage and/or displacement during the 2018 spring flood. Lived experiences were studied through focus groups with 20 residents and perceptions of community mental health and wellbeing were captured during key informant interviews with 10 local community leaders. Data collection and analysis employed grounded theory. Findings indicate that those who had residential displacement or damage due to the flooding experienced negative mental health impacts, both during and following the flood. While natural disasters have devastating impacts on mental health, the data also indicate that the communities were positively impacted by a collective and collaborative response to the flood. This paper argues for the utility of communal coping as a concept to describe the experiences of communities following residential damage and/or displacement following natural disasters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".