Social Representations Of Diseases Linked To Climate Change In The Population Of A Slum District: A Case Study From Haiti
Bibliographic record
Abstract
Faced with the threats posed by climate change to global public health in the 21st century, the island of Haiti has a duty to inform the population and disseminate knowledge on the health consequences of the phenomenon. The effects of climate change are imminent for the country. In terms of health, the consequences will particularly accentuate the prevalence of endemic diseases, water-borne and infectious pathologies, malnutrition and undernourishment. Also, information on this issue must be widely disseminated through environmental and health education in order to raise awareness in the population and encourage them to modify their daily lifestyles through mitigation and adaptation. Previous work on strategies for popularizing scientific knowledge has shown that culture and poverty constitute obstacles to changes in behavior favoring mitigation and adaptation to climate change. The study of the Social Representations of the populations or social groups concerned makes it possible to discarded them.. From this point of view, this article questions and analyzes the social representations of vector pathologies including Malaria, Dengue, Chikungunya and Zika among the residents of Jalousie, one of the vulnerable neighborhoods of the Metropolitan Region of Port-au-Prince (MRPP - Haiti). This work highlights the link established by the population of Jalousie between climate change and the transmission of the vector-borne diseases mentioned. It does this by considering elements of Haitian popular knowledge likely to build understanding that combines the prevention and symptomatology of these pathologies with knowledge of public hygiene and supernatural phenomena. The survey carried out on a representative sample of 121 residents of the Jalousie district, a slum area of MRPP, shows that vector-borne diseases are assimilated with epidemics and their transmission due to changes in the seasons (temperature change: hot weather, rainy weather in Haiti).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".