Discussing linkages between climate change, human mobility and health in the Caribbean: The case of Dominica. A qualitative study
Bibliographic record
Abstract
The Caribbean region is repeatedly exposed to extreme climate-related events, such as hurricanes and tropical storms, which are expected to increase in severity with climate change. This study aims to better understand how extreme climate events affect human mobility, social circumstances, and health-related issues in the Eastern Caribbean, focusing more specifically on Dominica, a Small Island Developing State (SIDS). Semi-structured qualitative interviews were conducted with people who were internally displaced following an extreme climate event in Dominica, and with people who migrated from Dominica to Guadeloupe. Mental health was a central issue discussed by participants. Some respondents raised issues regarding loss of livelihoods and poverty that affected their living conditions. For those who decided to migrate to Guadeloupe, the difficulties of getting migrant authorized status were very stressful. Other themes related to displacement trajectory, income, occupation, housing, access to food and water, health and psychosocial services, and the role of local and international assistance and social support and ties – that are well known social determinants of mental health, were raised by participants. Mental health and related determinants should be seen as a public health priority in Caribbean SIDS. Psycho-social interventions that focus on potential sources of vulnerabilities to mental health issues should be integrated in climate preparedness and response efforts. Otherwise, pre-existing social vulnerabilities may be aggravated, limiting the adaptation capacities of Caribbean SIDS to climate change. Public health and the health care system have a role to play in climate change adaptation.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".