“There’s Not Really Much Consideration Given to the Effect of the Climate on NCDs”—Exploration of Knowledge and Attitudes of Health Professionals on a Climate Change-NCD Connection in Barbados
Bibliographic record
Abstract
Despite widespread awareness of the rise of non-communicable diseases (NCDs) and the growing threat of climate change, little research has explored future health outcomes that will occur at the intersection of these challenges. Ten Barbadian health professionals were interviewed to assess their knowledge of health risks of climate change as it relates to NCDs in Barbados as a case study of a small island state at risk. There is widespread concern among health professionals about the current and future prevalence of non-communicable diseases among Barbadians. There is less concern about the future burden of NCDs in the context of a changing climate, largely because of a lack of knowledge among the majority of the health experts interviewed. Those knowledgeable about potential connections noted the difficulty that climate change would pose to the prevention and management of NCDs, given the impacts of climate stressors to food security, the built environment, and physiological and psychosocial health impacts. Lack of awareness among health professionals of the risk climate change poses to NCD prevalence and impact is reflective of the country's health priorities that fail to recognize the risk of climate change. We recommend efforts to disseminate information about climate change to stakeholders in the health sector to increase awareness.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".