The effect of guidance booklet on knowledge and attitudes of nurses regarding disaster preparedness at hospitals
Bibliographic record
Abstract
Background : Disasters have a potential of producing mass casualties thereby straining the health care systems. This means that hospitals need to be prepared for an unusual increase in workload, hence the importance of hospital disaster preparedness. Aim: The aim of the study to evaluate the effect of a guidance booklet on knowledge and attitude about disaster preparedness among nurses. Methods : Research design: A quasi experimental research design with pre-test post-test time series and follow up assessment. Setting: the study was conducted at University Hospital, in Menoufia Governorate, Egypt. Subjects: The convenience sample, it include all nursing managers (nursing directors and nursing supervisors n = 12), all head nurses (n = 48) and staff nurses (n = 280), available at the time of the study. They have all fulfilled the eligibility criterion of a working experience of not less than one year of the study settings. Tools of data collection: Two tools were used for data collection. Tool one: consists of three parts; part (a) to collect socio-demographics data and part (b) aimed to collect nurses' knowledge about general disaster, classification and disaster preparedness and part (c) aimed to assess the nurses' awareness by hospital disasters on external or internal level. Tool two: Attitudes of nurses towards disaster management plan. Results : The results of this study showed that, majority of nurses scored weak estimation in knowledge, awareness and attitudes level at pre-test measurement. Conversely, lowest percentages had moderate level, and good level of knowledge related to general disaster, while only 12.6% of nurses were satisfactory awareness about hospital disaster preparedness and 37.5% had positive attitude towards disaster management. There was statistically-significant ( p < .001). Conclusions : It was concluded that, guidance booklet was successful in achieving significant improvement in nurses ’ knowledge regarding disaster preparedness which was reflected in improvement and changing their attitude towards disaster. Recommendation: Continued nursing education should be open to all hospital staff according to their needs to increase their awareness about disaster preparedness.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".