Extreme weather events (EWEs)-Related health complications in Bangladesh: A gender-based analysis on the 2017 catastrophic floods
Bibliographic record
Abstract
Floods are major Extreme Weather Events (EWEs) that are more frequent and intense. Floods has multifarious dire impacts on human health, but health implications of floods are limitedly examined from a gender lens, particularly in developing countries like Bangladesh. Floods periodically hit in Bangladesh. The 2017 was a catastrophic year for Bangladesh. The year experienced two consecutive floods that were more catastrophic in the last couple of decades and direly affected 24 districts of the country. The floods resulted in health stress and intensifying exposure to manifold health vulnerabilities. Our study aimed to investigate gendered health complications caused by the floods and the impacts of the confluence of the floods and vulnerabilities relating to water, sanitation, health care facilities on reproductive health. To achieve this, we conducted 280 household surveys, 4 Focus Group Discussions, 4 In-Depth Interviews, and 6 Key Informant Interviews within the framework of mixed-method research in a northern flood-prone district named Jamalpur. Our findings showed that 84.6% of the respondents stated water gets polluted during floods, and 69.6% identified polluted water as a major challenge while collecting water during floods. Due to living with polluted floodwater, fever (66.4%) and diarrheal diseases (55.4%) were most common among women. In respect to reproductive health, 75% of the females reported improper menstrual management causing mental shocks and vaginal infections; over 66.4% females noted remaining without any measures. To mitigate health vulnerability, majority of the rural women (78.6%) encountered challenges – including the dearth of available medicine and poor transportation and communication. Health vulnerability also increased when poor communities failed to afford the cost of medicine because of poor economic condition and food insecurity. Consequently, our study recommends for fostering health education and the immediate deployment of health care facilities on an emergency basis to reduce health complications, especially among marginal groups (e.g., women and children). Future research can explore how the intersection of economic insecurity and flood whet differential health complications among poor and non-poor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".