“I think they should give primary health care a little more priority”. The primary health care in Caribbean SIDS: what can be said about adaptation to the changing climate? The case of Dominica— a qualitative study
Bibliographic record
Abstract
BACKGROUND: Adaptation to climate change (CC) is a priority for Small Island Developing States (SIDS) in the Caribbean, as these countries and territories are particularly vulnerable to climate-related events. Primary health care (PHC) is an important contributor to CC adaptation. However, knowledge on how PHC is prepared for CC in Caribbean SIDS is very limited. The aim of this paper is to discuss health system adaptation to climate change, with a focus on PHC. METHODS: We explored the perspectives of PHC professionals in Dominica on PHC adaptation to climate change. Focus group discussions (FGDs) were conducted in each of the seven health districts in Dominica, a Caribbean SIDS, between November 2021 and January 2022. The semi-structured interview guide was based on the Essential Public Health Functions: assessment, access to health care services, policy development and resource allocation. Data coding was organized accordingly. RESULTS: Findings suggest that health care providers perceive climate change as contributing to an increase in NCDs and mental health problems. Climate-related events create barriers to care and exacerbate the chronic deficiencies within the health system, especially in the absence of high-level policy support. Healthcare providers need to take a holistic view of health and act accordingly in terms of disease prevention and health promotion, epidemiological surveillance, and ensuring the widest possible access to healthcare, with a particular focus on the environmental and social determinants of vulnerability. CONCLUSION: The primary health care system is a key stakeholder in the design and operationalization of adaptation and transformative resilience. The Essential Public Health Functions should integrate social and climate and other environmental determinants of health to guide primary care activities to protect the health of communities. This study highlights the need for improved research on the linkages between climate events and health outcomes, surveillance, and development of plans informed by contextual knowledge in the SIDS.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".