The role of educating health-care personnel in prevention, diagnosis, or treatment of COVID-19: A narrative mini review
Bibliographic record
Abstract
Front-line clinicians and health-care workers need to be educated to provide care in critical situations such as large-scale catastrophes and pandemics. This narrative review is focused on investigating educational strategies in confrontation with coronavirus disease 2019 (COVID-19) pandemic. We conducted a literature search in December 2020 through LitCovid, PubMed, ERIC, and Cochrane Library in order to retrieve relevant studies regarding the role of education in prevention, diagnosis, and treatment of COVID-19. There were 12 reviewed studies related to this specific subject. The articles selected for this study demonstrated that education and training had a positive impact on the knowledge and attitude of the participants and also the educational interventions, whether they were simulation-based or other formats of training, would be deemed crucial for enhancing participants' level of perceptions and confidence. Therefore, it is highly recommended that public health policymakers consider this important issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".