Disaster nursing knowledge in earthquake response and relief among Nepalese nurses working in government and non-government sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background : Recently, the disasters in Nepal as elsewhere has caused a large number of deaths, injury and left hundreds of thousands of people homeless. It has also alerted all nurses to be prepared with adequate knowledge in order to respond to a disaster event effectively. This descriptive study aimed to describe and compare the level of knowledge in an earthquake disaster among Nepalese nurses working in government and non-government hospitals. Methods : Three hundred working registered nurses (RNs) were randomly selected from fourteen government and four non-governmental hospitals located in different parts of Nepal. Nurses’ knowledge in earthquake disaster was obtained through self-reported questionnaires. Descriptive and inferential statistics were used for data analysis. Results : The majority of the RNs worked in government hospitals (63.2%), more than half (59%) of the respondents had diploma level of education with the majority (66.3%) of them working in a hospital for less than six years. Two thirds (78%) had never attended disaster training drills and nearly half (47.7%) of the RNs determined that they themselves were not ready to face a future disaster. The knowledge of the RNs regarding earthquake disaster was at a moderate level (70.07 ± 10.01). The lowest score of nurses’ knowledge was related to assessment and triage in earthquake disaster response. Nurses working in governmental hospitals have a higher mean score of knowledge than those working in non-governmental hospitals ( P < .05). Conclusion : A disaster nursing training course should be provided for nurses particularly in non-governmental hospitals who had never received disaster training which will improve their knowledge in order to respond to 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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it