Understanding patient safety during earthquakes: a phenomenological study of disaster response
Bibliographic record
Abstract
BACKGROUND: Natural hazards, such as earthquakes, pose a significant risk to both the public and healthcare professionals, jeopardising patient safety due to the disruption of healthcare systems and services. This study aimed to explore the lived experiences of healthcare professionals concerning patient safety during natural hazards, specifically earthquakes. METHODS: Employing a descriptive phenomenological approach, the study followed the Consolidated Criteria for Reporting Qualitative Research guidelines. 23 participants, including doctors, nurses and paramedics, were interviewed using purposive sampling. Data were gathered through semistructured interviews, which were audio recorded and transcribed. Ethical approval was obtained, and Colaizzi's method was used for data analysis, with findings validated through researcher consensus and participant feedback. RESULTS: Nine overarching themes emerged, such as the emotional toll of communication breakdowns, struggles with patient identification, stress due to resource scarcity, operational chaos, ethical dilemmas and psychological impacts on both patients and staff. The study found that these factors collectively influenced patient safety during the earthquake. CONCLUSION: The emotional strain caused by communication failures, patient identification issues and resource shortages compounded the challenges of providing safe care during the earthquake. Strengthening disaster preparedness through improved communication systems, resource management, psychological support, interagency coordination and regular realistic disaster drills is essential for safeguarding patient safety in future disasters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".