Informal Caregiving and Disaster Risk Reduction: A Scoping Review
Bibliographic record
Abstract
Abstract Informal caregivers are a population currently in the shadows of disaster risk reduction (DRR), and yet essential to the provision of healthcare services. This scoping review explored the literature to understand issues related to informal caregiving and promising practices to support resilience for disasters. Following guidelines for scoping review as outlined by Tricco et al. (2016), relevant publications were identified from five major databases—Medline, Embase, PubMed, Web of Science, and Scopus. Relevant studies referenced informal caregiving and disasters for a variety of population groups including children, people with disabilities or chronic illnesses, and older adults. Studies were excluded if they discussed formal caregiving services (for example, nursing), lacked relevance to disasters, or had insufficient discussion of informal caregiving. Overall, 21 articles met the inclusion criteria and were fully analyzed. Five themes were identified: (1) the need for education and training in DRR; (2) stressors around medication and supply issues; (3) factors affecting the decision-making process in a disaster; (4) barriers leading to disaster-related problems; and (5) factors promoting resilience. Recommended areas of strategic action and knowledge gaps are discussed. Many informal caregivers do not feel adequately prepared for disasters. Given the important role of informal caregivers in healthcare provision, preparedness strategies are essential to support community resilience for those requiring personal care support. By understanding and mobilizing assets to support the resilience of informal caregivers, we also support the resilience of the greater healthcare system and the community, in disaster contexts.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".