Scrambling For Safety In The Eye Of Dorian: Mental Health Consequences Of Exposure To A Climate-Driven Hurricane
Bibliographic record
Abstract
As climate change alters the behavior of Atlantic hurricanes, these storms are trending stronger, wetter, and slower moving over coastal and island populations. Hurricane Dorian exemplified all three attributes. Dorian's destructive passage over the Abaco Islands, Bahamas, on September 1, 2019, exposed residents of its capital, Marsh Harbour, to a prolonged encounter with the storm's core. After Dorian's fierce front eyewall and towering storm surge tore apart shanty town habitats and eviscerated concrete homesites, residents desperately sought refuge during the brief respite when Dorian's eye passed directly overhead. The category 5 winds then resumed abruptly and Dorian continued its relentless destruction. This article focuses on the storm's mental health consequences, drawing on observations of on-site clinicians as well as findings from previous research on the mental health effects of Atlantic hurricanes and the transformation of hurricane hazards resulting from climate change. To protect island and coastal populations against climate-driven storms, disaster planning policy should emphasize resilience-focused prevention and mitigation strategies. In the aftermath of these events, health system response should include community outreach, case finding, and evidence-based interventions that optimize the use of mental health professionals.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".