A multi-national assessment of physicians’ perspectives on air pollution and respiratory health
Bibliographic record
Abstract
Introduction: Air pollution is a major threat to human health, globally, but the awareness among physicians and the advice that they offer to patients remain poorly understood. Aims: To assess the perspectives of physicians specialized in general practice or pulmonology on the prevention and management of respiratory disease (RD) due to air pollution. Methods: A mixed-methods computer-assisted telephone interview among 600 purposively selected physicians in Brazil, Canada, China, France, Germany, India, Mexico, and the United States (USA). Results: Air pollution was recognized by 71% of physicians as a major risk factor to RD development, but education on air pollution was prioritized by 21%. Normal monitoring of respiratory health without preventative action for RD-susceptible patients was prioritized by 39% of physicians in China (figure 1). Compared to pulmonologists (92%), general practitioners (80%) were less confident in their knowledge of monitoring and management strategies (p<0.001). In qualitative responses, participants offered limited strategies (e.g., masks, ventilation) to monitor and minimize patients’ exposure to air pollutants. erj;66/suppl_69/OA5375/F1 F1 F1 Conclusions: To protect the respiratory health of all patients, general practitioners should improve their knowledge and confidence regarding strategies to educate patients on air pollution and help them implement comprehensive interventions appropriately addressing RD-risk.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".