What can be said about risks, vulnerabilities, and adaptation to climate change in Caribbean small island developing states (SIDS)? The case of Dominica. A qualitative study
Bibliographic record
Abstract
IntroductionCaribbean Small island developing states (SIDS) are generally qualified as disproportionately vulnerable to climate change, including extreme weather events like hurricanes. While many studies already documented the impacts of climate change on health in the wealthiest countries, there is little knowledge in this field in Caribbean SIDS. Our study aims to discuss health risks and vulnerabilities in a Caribbean context to inform future adaptation measures to climate change.MethodsOur paper is based on a qualitative study that was conducted in Dominica, a Caribbean SIDS. The data come from semi-structured interviews organized between March 2020 and January 2021 with people internally displaced following an extreme climate event, either tropical storm Erika (2015) or Hurricane Maria (2017), and with some people who migrated to Guadeloupe after Hurricane Maria. Interview guides were based on conceptual frameworks on climate change, migration and health, and vulnerability to climate change. Data were analyzed deductively based on frameworks and inductively to allow new codes to emerge.ResultsOur findings suggest that current knowledge of climate change by those who have been displaced by an extreme climate event varied greatly depending on the education level, class, and socioeconomic condition of the participant. Participants experienced various negative consequences from a storm or hurricane such as increased risk of relocation, lack of access to healthcare, and food, job, and water insecurities – all circumstances know to correlate with mental health issues. Participants suggested stronger dwellings, community preparedness committees to act sooner, and climate change sensitization and awareness campaigns to foster community unity and solidarity.ConclusionThese findings contribute to the perspectives and knowledge of climate change, highlighting that existing extreme climate event committees and government officials need to address structural and social barriers that can potentially increase social inequalities and lead to maladaptation to climate change with potential consequences on public health.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".