Hospital Preparedness in Facing COVID-19 Pandemic: A Systematic Literature Review
Bibliographic record
Abstract
During the ongoing global COVID-19 pandemic, the number of cases continues to increase and leads to a surge of patients who need treatment. However, many hospitals are not ready to deal with this type of emergency. This study aimed to review previous studies on Hospital preparedness in facing COVID-19 disaster. This is a Systematic Literature Review on articles collected from 4 databases, i.e., PubMed, Science Direct, Google Scholar, and Crossref by applying the inclusion criterion of articles published in English and Indonesian on qualitative and quantitative studies related to hospital preparedness. PRISMA Guideline was used for this review. Based on the application of the inclusion criterion, eight articles were considered to be appropriate for the review. Three of these articles presented good hospital preparedness, while the other five demonstrated the presence of gaps in terms of facilities, staff training, and coordination. The instruments used in the study presented in these articles were adapted from CDC and WHO and were modified to adjust them with the local condition. A comprehensive assessment on hospital preparedness is needed. Health care worker training is an important step in hospital 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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".