Awareness and Preparedness of Field Epidemiology Training Program Graduates to Respond to COVID-19 in the Eastern Mediterranean Region: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The Field Epidemiology Training Program (FETP) is a 2-year training program in applied epidemiology. FETP graduates have contributed significantly to improvements in surveillance systems, control of infectious diseases, and outbreak investigations in the Eastern Mediterranean Region (EMR). OBJECTIVE: Considering the instrumental roles of FETP graduates during the coronavirus disease (COVID-19) crisis, this study aimed to assess their awareness and preparedness to respond to the COVID-19 pandemic in three EMR countries. METHODS: An online survey was sent to FETP graduates in the EMR in March 2020. The FETP graduates were contacted by email and requested to fill out an online survey. Sufficient number of responses were received from only three countries-Jordan, Sudan, and Yemen. A few responses were received from other countries, and therefore, they were excluded from the analysis. The questionnaire comprised a series of questions pertaining to sociodemographic characteristics, knowledge of the epidemiology of COVID-19, and preparedness to respond to COVID-19. RESULTS: This study included a total of 57 FETP graduates (20 from Jordan, 13 from Sudan, and 24 from Yemen). A total of 31 (54%) graduates had attended training on COVID-19, 29 (51%) were members of a rapid response team against COVID-19, and 54 (95%) had previous experience in response to disease outbreaks or health emergencies. The vast majority were aware of the main symptoms, mode of transmission, high-risk groups, and how to use personal protective equipment. A total of 46 (81%) respondents considered themselves well prepared for the COVID-19 outbreak, and 40 (70%) reported that they currently have a role in supporting the country's efforts in the management of COVID-19 outbreak. CONCLUSIONS: The FETP graduates in Jordan, Sudan, and Yemen were fully aware of the epidemiology of COVID-19 and the safety measures required, and they are well positioned to investigate and respond to the COVID-19 pandemic. Therefore, they should be properly and efficiently utilized by the Ministries of Health to investigate and respond to the current COVID-19 crisis where the needs are vastly growing and access to outside experts is becoming limited.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".