"There was no advantage to being old in the shelter": experiences of older adults to tropical cyclone in the Coastal Bangladesh
Bibliographic record
Abstract
Abstract Over the past decade, climate-induced calamities have become more frequent in coastal regions around the world, causing immense loss of human life. Across age groups, older adults are among the most vulnerable to climate-related disasters. Despite this, the perspectives of older adults remain underrepresented in disaster risk management frameworks. Because understanding the unique needs and contributions of specific populations in climate-vulnerable countries is essential in the design of equitable and effective disaster management strategies, this qualitative study explores the experiences of elderly individuals of evacuation, shelter living, and access to support services during tropical cyclones in Coastal Bangladesh. Data were collected through 15 in-depth interviews and two focus group discussions with individuals residing in cyclone-prone areas. The findings reveal that elderly individuals in cyclone shelters often face widespread discrimination and neglect. Low social status, a lack of age-sensitive infrastructure, and inadequate mobility support hinder their access to essential services. Many participants reported feeling invisible during relief operations and suffering psychological distress due to both the disaster and the treatment they received while displaced. The study underscores critical shortcomings in current disaster response strategies and recommends integrating age-inclusive measures into disaster planning and response, such as the development of accessible shelters, the training of first responders in age sensitivity, and the formal involvement of older adults in community-based emergency preparedness initiatives.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".