Physician perspectives on the burden and management of asthma in six countries: The Global Asthma Physician Survey (GAPS)
Bibliographic record
Abstract
BACKGROUND: Despite recognition of asthma as a growing global issue and development of global guidelines, asthma treatment practices vary between countries. Several studies have reported patients' perspectives on asthma control. This study presents physicians' perspectives and strategies for asthma management. METHODS: Physicians seeing ≥4 adult patients with asthma per month in Australia, Canada, China, France, Germany, and Japan were surveyed (N=1809; ≈300 per country). A standardised questionnaire was developed for this study and administered by telephone, online or face-to-face. Statistics were weighted to account for the sampling scheme. RESULTS: Physicians estimated that 71% of their adult patients received maintenance medication, with adherence monitored by 76-97% of physicians. Perceived major barriers to patient adherence included: patients taking treatment as needed; acceptance of symptoms; and patients not perceiving treatment benefits. Written action plans (37%) and technology (15%) were seldom employed by physicians to aid patients' asthma management. Physicians rarely (10%) used validated patient-reported questionnaires to monitor asthma control, instead monitoring selected symptoms, exacerbations, and/or lung function measurements. Awareness of single maintenance and reliever therapy (SMART/MART) varied among countries (56-100%); although most physicians (72%) had prescribed SMART/MART, the majority (91%) co-prescribed a short-acting bronchodilator at least some of the time. CONCLUSIONS: These results show that physicians generally do not employ standardised tools to monitor asthma control or to manage its treatment and that despite high awareness of SMART/MART, the strategy appears to be commonly misapplied. Better education for patients and physicians is required to improve asthma management and resulting patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".