Knowledge, Attitude, and Practice of Healthcare Providers Toward Novel Coronavirus 19 During the First Months of the Pandemic: A Systematic Review
Bibliographic record
Abstract
Introduction: Coronavirus disease (COVID-19) is a highly contagious viral infection that has spread to every corner of the world. Lack of knowledge among healthcare providers (HCPs) about diseases such as COVID-19 may delay the diagnosis, disease spread, and produce poor infection control practices. Hence, this systematic review aimed to summarize the knowledge, attitudes, and practices (KAP) of HCPs toward COVID-19 during the first months of the pandemic. Methods: A systematic review was conducted according the PRISMA guidelines, and the protocol was registered on PROSPERO (CRD42020191742). A relevant article search was performed on EMBASE, PubMed, CINAHL, Scopus, and the Google Scholar database. The methodological quality of studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. The median percentage of HCPs with good KAP was computed. Results: Twenty studies involving 12,072 HCPs were included in the review process. Median percentages of 75.8% (IQR: 69.3–87.7%), 74.6% (IQR: 54.4–74.6), and 79.8% (IQR: 67.0–79.8%) of HCPs had good knowledge, and positive attitude and practice, respectively. Although the reported risk factors were inconsistent among studies, age, gender, level of education, experience, infection prevention training, and sources of information were associated with knowledge of HCPs. In addition, being elderly, having a high level of education, absence of chronic illness, and good knowledge and practice were significantly associated with the attitude of HCPs. Further, types of profession, experience, age, level of education, use of personnel protective equipment, and gender were significantly associated with the practice of HCPs. Conclusions: Approximately, three-fourths of HCPs had good knowledge, attitudes, and practices toward COVID-19 during the first months of the pandemic, although the percentage of HCPs was inconsistent in different study settings. In addition, associated factors of KAP were inconsistent among studies; hence, stake holders should target locally identified risk factors to design relevant education packages and infection prevention training to halt the rapid transmission of COVID-19. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020191742 , identifier: CRD42020191742.
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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 | 0.012 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".