Risks of electromagnetic fields from the perspective of general practitioners and pediatricians
Bibliographic record
Abstract
BACKGROUND: Although there is little evidence for adverse health effects due to exposure to electromagnetic fields (EMF) below legal limits, worries regarding these effects are relatively frequent in the general population. For many individuals, general practitioners (GPs) and pediatricians are the first point of contact with the health system. Therefore, it is essential to understand their EMF risk perception. METHODS: We conducted a cross-sectional mixed methods study inviting 3,000 GPs and 2,000 pediatricians sampled from the German Federal Medical Registry, of which 614 (12.3%) participated in an online survey and 25 participated in focus groups. We estimated the prevalence of high risk perception, poor subjective knowledge regarding EMF, and the relevance of EMF in their everyday work correcting for non-response by Multilevel Regression and Poststratification. RESULTS: About a quarter of physicians indicated high risk perception regarding health and EMF. Relevance was low, with about 40% of GPs and about 20% of pediatricians reporting EMF-related consultations during the last year. About 60% of physicians had poor subjective knowledge. Many physicians said they could not rule out the possibility of adverse health effects of EMF due to insufficient knowledge and expressed a need for information to address this knowledge gap. CONCLUSIONS: A substantial part of GPs and pediatricians with high risk perception are physicians with poor subjective knowledge regarding EMF who cannot completely rule out EMF below legal limits as a cause of unspecific, unclear symptoms, and who are therefore open to patients' suggestions of EMF as a potential cause.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".