Low to medium-low risk perception for dengue, chikungunya and Zika outbreaks by infectious diseases physicians in France, Western Europe
Bibliographic record
Abstract
BACKGROUND: Many tropical countries are currently experiencing dengue (DEN), chikungunya (CHIK) and also more recently Zika (ZIKA) epidemics (particularly in Latin America). Although the risk of transmission and spread of these infections in temperate regions remains a controversial issue, vector-borne diseases have been widely reported in the media and have been the focus of preventive strategies by national and international policy-makers and public health authorities. In this context, we wanted to determine the extent of risk perception in infectious diseases (ID) physicians of the current and future risk of arboviral disease introduction, autochthonous case development and epidemic scenarios in France, Western Europe. METHODS: To this aim, we developed an original standardized questionnaire survey which was disseminated by the French Infectious Diseases Society to ID physician members. RESULTS: We found that ID physicians perceived the risk of introduction and outbreak development of DEN, CHIK and ZIKA in France to be low to medium-low. Generalized Linear Model(s) identified medical school training, the extent of professional experience, and awareness of the French national plan regarding arboviral infections as significant predictors for lower risk perception among respondents. CONCLUSION: Despite the fact that arboviral diseases are increasingly being imported into France, sometimes resulting in sporadic autochtonous transmission, French ID physicians do not perceive the risk as high. Better communication and education targeting health professionals and citizens will be needed to enhance the effectiveness of the French national plan to prepare against arboviral diseases.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".