Public Health Workers’ Knowledge, Attitude, and Practice Regarding COVID-19: The Impact of the Field Epidemiology Training Program in the Eastern Mediterranean Region
Bibliographic record
Abstract
Background Globally, there is a growing need for public health professionals skilled in preventing and responding to the surge of emerging and re-emerging infectious diseases. This is particularly important to the Eastern Mediterranean countries that are facing emergencies in addition to the increased public health risks of unprecedented scale during the COVID-19 pandemic. Public health professionals are instrumental in responding to the COVID-19 pandemic in terms of detecting and monitoring new cases, conducting investigations, tracing contacts, ensuring patients are being tested, applying isolation and quarantine protocols, providing up-to-date information, educating the community, and producing statistics and models to track disease progression. Objective This study aims to compare knowledge, attitude, and practice (KAP) regarding COVID-19 between public health workers (PHWs) that attended the Field Epidemiology Training Program (FETP trained) and those who did not attend FETP (non-FETP trained). Methods A multicountry cross-sectional survey was conducted among PHWs who participated in the COVID-19 pandemic in 10 countries in the Eastern Mediterranean Region. An online questionnaire that included demographic information and KAP regarding the COVID-19 pandemic was distributed among PHWs. The scoring system was used to quantify the answers; bivariate and multivariate analyses were performed to compare FETP-trained with non-FETP–trained PHWs. Results Overall, 1337 PHWs participated, with 835 (62.4%) <40 years of age and 851 (63.6%) male participants. Of them, 423 (31.6%) were FETP trained, including 189 (44.7%) at an advanced level, 155 (36.6%) at an intermediate level, and 79 (18.7%) had basic level training. Compared to non-FETP–trained participants, FETP-trained participants were older and had higher KAP scores. FETP participation was low in infection control and public health laboratories. KAP mean scores for intermediate-level attendees were comparable to the advanced level. Conclusions FETP-trained participants had better KAP than non-FETP–trained PHWs. Expanding the intermediate level, maintaining the rapid response training, and introducing the laboratory component are recommended to maximize the benefit from the FETP. Infection control, antimicrobial resistance, and coordination are areas where training should be included.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".