Perceptions of coastal dwellers about the effects of extreme temperature and saline water on human health: evidence from Bangladesh
Bibliographic record
Abstract
Background Climate change disproportionately affects coastal communities worldwide, increasing exposure to extreme temperature and saline water intrusion. Understanding these impacts is critical for public health planning and intervention. This study aims to examine the perceptions about the effects of climate change-induced phenomena, specifically extreme temperature and water salinity, on the overall human health of coastal communities residing in Ramgati, Lakshmipur, Bangladesh. Methods The study adopted a quantitative research approach and utilized a cross-sectional survey design to gather data. The sample consisted of 391 participants (N = 391) residing in the coastal region of Ramgati. A structured questionnaire was employed to collect data. The gathered data were subjected to several bivariate analyses, including independent-sample t-tests, Pearson correlation analysis, and hierarchical regression analysis, using IBM SPSS version 24.0. Results Participants exposed to higher extreme temperature reported their perceptions of various health effects, such as heat stroke, dengue epidemic, migraine and headache. Additionally, participants experiencing elevated water salinity reported different health effects, including hair loss, high blood pressure, diarrhea, maternal health problems, child development, and hindered child mental health development. Linear regression analysis revealed that participants’ age (β = 0.33, p < 0.001), gender (β = −0.16, p < 0.001), perceived risk of health diseases (β = 0.17, p < 0.001), high salinity in water (β = 0.15, p = 0.002), and high temperature (β = 0.25, p < 0.001) were significantly associated with the perception of health effects. Conclusion This study highlights the importance of addressing key issues regarding the effects of extreme temperature and saline water on human health. Specifically, the study reports on access to clean drinking water, climate change adaptation strategies, health education and awareness, an integrated public health approach, and the needs of vulnerable populations, in order to mitigate the effects of climate change on human health living in the coastal areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".