Nurses' experiences of ethical preparedness for public health emergencies and healthcare disasters: A systematic review of qualitative evidence
Bibliographic record
Abstract
Little is known about nurses' direct experiences of ethical preparedness for dealing with catastrophic public health emergencies and healthcare disasters or the ethical quandaries that may arise during such events. A systematic literature review was undertaken to explore and synthesize qualitative research literature reporting nurses' direct experiences of being prepared for and managing the ethical challenges posed by catastrophic public health emergencies and healthcare disasters. Twenty-six research studies were retrieved for detailed examination and assessed by two independent reviewers for methodological validity prior to inclusion in the review. Of these, 12 studies published between 1973 and 2011 were deemed to meet the inclusion criteria and were critically appraised. The review confirmed there is a significant gap in the literature on nurses' experiences of ethical preparedness for managing public health emergencies and healthcare disasters, and the ethical quandaries they encounter during such events. This finding highlights the need for ethical considerations in emergency planning, preparedness, and response by nurses to be given more focused attention in the interests of better informing the ethical basis of emergency disaster management.
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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 | 0.026 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".