Developing and validating a nursing strategic plan for COVID-19 pandemic
Bibliographic record
Abstract
Background: Although there were many cautions about a probable pandemic, the health organizations have not prepared to deal with the COVID-19 pandemic. Coronavirus outbreak is a dangerous threat to humanity. An essential concern to control this pandemic is to outline an action plan, set an evaluation frame, identify monitoring techniques, detect measures, and recognize key performance factors and investigations providing evidence-based information.Methods: The descriptive and methodological design was used to achieve the objectives of the present study. This study was conducted at Zagazig University Hospitals (ZUH’s), Egypt. Three types of samples were used: A convenience sample (n = 110) including the nursing leaders, a stratified proportionate random sample (n = 302) from different categories of nurses, and a Jury committee (n = 9). One 1Questionnaire format and 2 opinionnaire sheets were utilized for data collection.Results: Statistically significant differences were found between nurses and nursing leaders concerning the dimensions of vision (p-value .000), mission (p-value .006), SWOT analysis (p-value .008), goals and objectives (p-value .000), the lines of business (p-value .000), the strategic business units (p-value .000), general strategic items(p-value .013), and action plan (role of nursing staff during the epidemic, p-value .000; immediate evacuation system, p-value .000; training, and hospital status during the epidemic, p-value .000; availability of the necessary equipment, supplies, and tools to face the epidemic and consumables, p-value .000; precautions inside the hospital, p-value .010; and infection control, p-value .002). However, there was no significant difference between nurses and nursing leaders regarding dimensions of planning for the planning, values, the key performance indicators, and the evaluation of the nursing strategic plan for COVID-19.Conclusion and recommendations: The questionnaire format of assessing nurses’ awareness about the nursing strategic plan for COVID-19 is reliable, valid, and usable. Nurses’ awareness about a nursing strategic plan for COVID-19 was generally poor and needs to be raised. The nursing strategic plan for COVID-19 was developed and validated. The suggested strategic plan for COVID-19should be utilized at ZUH's. ZUH's should allocate the needed and required resources for the application of the recommended plan for overcoming COVID-19 or any future occurrences.
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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 | 0.078 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".