Inhaler user awareness of climate implications: a Canadian survey
Bibliographic record
Abstract
Background: People with lung diseases are vulnerable to climate change; yet the most common inhaler device, metered dose inhalers (MDIs), have significant climate impacts. Aim: To understand inhaler users’ perspectives on climate change and awareness of inhaler climate implications. Methods: Canadians (aged ≥ 16) who reported using an inhaler in the previous 6 months were invited via health organizations' newsletters to complete a cross-sectional e-survey. Multivariate regression models assessed the association between sociodemographic factors and climate change risk perception index scores, inhaler disposal methods, awareness of inhaler climate impacts, and willingness of MDI users to switch to low carbon footprint devices. Results: There were 255 respondents (mean age 68 ± 12 years, 64% female, 80% MDI users). Most individuals were concerned about climate change (84%) but only 20% were aware that MDIs have high carbon footprints. Older individuals and women were less likely to be aware of the carbon footprint of MDIs, while higher education was associated with greater awareness (p<0.05). People who already experienced health changes due to climate events (31%) had higher climate change risk perception scores (p < 0.001). Most respondents reported disposing of their inhalers in garbage bins (58%) and when provincial pharmacy return programs were available (n=216) they were underutilized (26%). Nearly all MDI users (92%) were willing to switch to a lower carbon footprint device. Conclusions: Inhaler users are concerned about climate change but lack awareness of inhaler climate impacts. Interventions that promote education, use of low carbon devices, and sustainable disposal could reduce inhaler-related climate impacts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".