Knowledge, Perceptions, and Attitudes of Israeli Healthcare Professionals Toward Mpox: A Survey-Based, Cross-Sectional Study
Bibliographic record
Abstract
Background: Mpox, a zoonotic viral disease, has recently emerged as a significant global public health challenge. Historically confined to endemic regions in West and Central Africa, recent outbreaks in non-endemic areas have highlighted the critical role of healthcare professionals (HCPs) in disease management and prevention. Methods: This cross-sectional study surveyed 709 Israeli HCPs, with a mean age of 40.6 ± 9.4 years, to evaluate their knowledge, perceptions, and attitudes toward mpox and identify gaps to inform educational and public health strategies. Results: Findings revealed that only 38.9% of respondents demonstrated good knowledge about mpox, while 61.1% exhibited poor knowledge. Misconceptions were prevalent: 37.9% identified paracetamol as a management option, with only 31.0% recognizing the need for antivirals. Notably, 67.1% correctly identified mpox as a viral disease, yet only 47.2% were aware that lymphadenopathy is a distinguishing symptom. Multivariable analysis identified several predictors of good knowledge, including marital status, being a medical doctor (versus an allied health professional), work seniority, and previous exposure to mpox-related information. Attitudes varied, with 57.7% expressing confidence in the ability of global health authorities to control mpox, and 59.0% expressing confidence in the Israeli Ministry of Health. Interest in learning more about mpox and related topics was high, with 67.4% showing interest in the epidemiology of emerging diseases. Respondents with good knowledge consistently exhibited more positive attitudes and confidence in managing mpox-related challenges. Conclusions: These findings underscore the urgent need for targeted educational interventions to enhance HCPs’ readiness and response capabilities. Strengthening professional training, incorporating emerging infectious diseases into curricula, and leveraging accurate media communication are critical steps toward improving preparedness for mpox and future outbreaks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".