Physicians’ knowledge, attitudes and practices towards Zika virus infection in Jordan
Bibliographic record
Abstract
INTRODUCTION: Zika virus (ZIKAV) disease is a public health problem of international concern. Recent evidence has documented imported ZIKAV cases into the Middle East and the existence of ZIKAV-transmitting mosquitoes in Jordan. However, limited data exist on the role of physicians in public awareness in this regard. This study aimed to assess ZIKAV knowledge, attitudes and counseling practices (KAP) of general physicians and gynecologists in Amman, Jordan. METHODOLOGY: In this cross-sectional study, a structured paper-based questionnaire was completed by 119 participants during 2016-2017. RESULTS: Only 4.2% of the physicians correctly addressed ZIKAV-complication questions. A misconception of considering direct contact between individuals and breastfeeding as modes of ZIKAV transmission was observed. Only one participant correctly recognized that isolation of infected or exposed persons is not recommended. Having at least five years of experience in medical practice was the only factor that was significantly associated with a high knowledge score (P-value=0.011). Although prevention measures are the sole method to control ZIKAV spread, only 50% of participants believed in the efficacy of such measures. Despite a quarter of participants perceiving ZIKAV as a threat to their patients, none of them have counseled a patient in this regard before. The presence of an evidence of ZIKAV in Jordan and health authorities' recommendations were the most important predictors for adoption of counseling practice. CONCLUSIONS: General physicians and gynecologists in Jordan had several gaps in knowledge of key aspects of ZIKAV disease, and there is a need for specific training programs of physicians and gynecologists.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".