Preparedness Assessment for Disaster Management Among Dhahran Al Janoub General Hospital Staff During Hazm Storm Support 1436/2015
Bibliographic record
Abstract
The hospital staff need to be competent to utilize the disaster plan to cope up with an emergency situation. Therefore, the present study has aimed to assess the knowledge of hospital staff of Dhahran Al Janoub General Hospital regarding the disaster management during Hazm Storm Support 1436/2015 in Saudi Arabia. The study has employed quantitative research design to assess the disaster management of hospital staff by recruiting 84 individuals (physicians, nurses, technicians, officers, and housekeepers) from Dhahran Al Janoub General Hospital. A questionnaire was given to respondents to gather information about disaster management. The obtained data was analysed using SPSS through chi-square analysis. The study results clearly depicted that the hospital staff with fewer years of experience had lesser knowledge about the disaster assessment as compared to the experienced employees. There was no statistically significant relationship identified between different job categories in the hospital and the level of knowledge about presence or absence of the emergency response plan. However, there was a statistically significant association found between different job professions and level of awareness regarding presence or absence of hospital command centres. The study concluded that the knowledge of emergency preparedness among the hospital staff was moderate and the hospital staff should participate and seek opportunities to prepare assessment for disaster management.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".