Climate Impact of Inhalers: Patient Empowerment Drives Prescription Change
Bibliographic record
Abstract
Studies show patients are unaware of the climate impact of metered dose inhalers (MDI) vs. dry powder inhalers (DPI) but are open to change inhalers. We aimed to assess the efficacy of empowering patients to change inhaler for environmental reasons. Stable asthmatic patients on MDI (salbutamol) were given an educational pamphlet and a pre-filled DPI (terbutaline) prescription. Primary outcome was MDI to DPI rotation at 30 days. Secondary outcomes were rotation back from DPI to MDI at 90 days and patient and provider perspectives. Odds of rotation were assessed with multivariate logistic regressions. We enrolled 54 patients (mean age 57) with a mean asthma control test [ACT] score of 20 and surveyed 7 providers. Higher health literacy correlated with better asthma control (p=0.007). Both groups had low awareness of inhalers' climate impact (94% patients, 71% providers) but valued reducing carbon footprint (70% patients, 57% providers) and ease of use (67% patients, 71% providers). 56% of patients and 43% of providers considered inhaler’s climate impact important. Fewer patients (43%) than providers (57%) attributed high importance to cost. Rate of rotation from MDI to DPI 24%, of whom 13% switched back to MDI at 90 days. Poor asthma control (ACT score < 20) increased odds of rotation from MDI to DPI (OR 7.5; 1.57-47.62 95% CI) after adjustment for covariates. Patients who felt empowered to decrease their carbon footprint were more likely to change inhalers (OR 2.4; 1.08- 6.07 95% CI). Empowering patients to initiate an MDI to DPI transition was a successful approach to reduce the climate impact of inhalers. Patients and providers value lowering climate impact, and poor asthma control doesn't hinder inhaler changes.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".