The vulnerability of the Iranian elderly in disasters: Qualitative content analysis
Bibliographic record
Abstract
BACKGROUND: Elderly people are among the most vulnerable groups in natural disaster events. Although old age is responsible for them becoming unequally vulnerable, understanding the different aspects of vulnerability can help health care providers, especially nurses, to manage disaster risk for this increasing number of people. This study intended to explore disaster-related vulnerability and its contributing factors based on older adults' perceptions and experiences. MATERIALS AND METHODS: This qualitative content analysis study was performed in Iran in 2016. The study was conducted by semi-structured interviews of 24 participants, and purposeful sampling with maximum variation continued until data saturation. RESULTS: By analyzing primary codes two main themes were extracted through content analysis, namely personal factors and social factors, from experiences of two experts in the field of health in emergencies and disaster management among 22 Iranian elderly participants. CONCLUSIONS: This study indicated that age is not the only criteria that makes an elderly person vulnerable, but their lifetime achievements and experiences can determine their level of vulnerability. The results of this study will help health service providers as well as disaster nurses to identify and moderate the factors affecting the vulnerability of the elderly, and by using their rich experience, enhance senior citizens' resilience to disasters.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".