Scoping Review of Kidney Patients and Providers Perspectives on Disaster Management
Bibliographic record
Abstract
Introduction: Patients with kidney disease are uniquely vulnerable to disasters and the need to understand stakeholder experiences to improve disaster preparedness has been highlighted. We aimed to explore the existing literature capturing patient and provider perspectives, identify research gaps, and develop research priorities in disaster management. Methods: This was a scoping review of the empirical literature that has explored the lived experience and preparedness of patients, caregivers, or healthcare professionals during natural or human-caused disasters. A content analysis using an inductive approach was conducted. Results: = 1, related to the Russian invasion of Ukraine). The outcomes examined were variable focusing on the following 4 aspects of disaster management: (i) identifying patient-level issues (preparedness, personal challenges, and psychosocial impact); (ii) damage assessment (infrastructure and equipment, personnel, and patient outcomes); (iii) response assessment (hemodialysis treatments delivered or missed, delivery of other kidney replacement therapies, and identifying practice gaps); and (iv) system assessment (examining capabilities and addressing surge capacity). The studies were at risk of survivor bias and most only used an investigator-designed survey for data collection. There was a dearth of evidence capturing the perspectives of caregivers, and pediatric and other vulnerable patients. Conclusion: The literature examining patient and provider perspectives or experiences is scarce and at risk of bias. Methodological, population, outcome, process, and impact priorities are proposed to guide future research initiatives and generate evidence to inform context and disaster-specific relief efforts.
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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 | 0.024 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".