Synthesis of literature on strategies for chronic disease management post disasters
Bibliographic record
Abstract
Aims. Disasters are devastating events that can overwhelm individuals with chronic diseases and shift their priorities from routine disease management to immediate survival needs. There were fatal consequences to this shift in health management during recent disasters. The following review of literature was conducted to identify strategies to help manage chronic illness during disasters. Background. During several recent disasters many people with pre‐existing chronic diseases sought help at disaster shelters. These individuals experienced hardships due to difficulty contacting physicians, lack of medications, insufficient insurance coverage, lack of transportation, and inadequate resources in shelters. Method. The Ecological Model of Disaster Management guided the selection and synthesis of research articles focused on the management of adults with chronic diseases during disasters. Articles published between January 2000–May 2009 were included. Research focused on mental illness, children or adolescents was excluded. Findings. The sample included five surveys, five retrospective record audit, four qualitative studies and one study with correlational study design. The review yielded recommendations for disaster planning for individuals with chronic diseases. Conclusions. Integration of beneficial strategies, informed by the literature and tailored to characteristics of the chronic disease population can provide effective solutions for chronic disease management post disaster. Technology’s role in facilitating chronic disease management during disasters is discussed. Relevance to clinical practice. Being the most plentiful health care providers and adept at working with individuals with chronic disease, nurses are in an excellent position to take leading roles in disaster planning missions. Educating individuals on self‐management techniques, participating in drills, and familiarising themselves with disaster technology are some of the ways nurses can help and support individuals with chronic diseases during disasters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".