The Air We Share: Guiding Inhaler Selection Today for a Sustainable Tomorrow
Bibliographic record
Abstract
Climate change is currently the greatest global health threat. As the planet experiences rising temperatures due to global warming, widespread health impacts are emerging, including extreme weather events, food insecurity due to droughts, and forced relocation of populations. From a respiratory health perspective, patients with lung disease experience negative health consequences due to high temperatures, risks from heat-related illnesses, exposure to wildfire smoke that causes poor air quality, and increased severity and duration of pollen seasons. The resulting health consequences contribute to increased use of healthcare services, which in turn generate their own contributions to healthcare pollution, thus further worsening the climate crisis. In fact, the health sector contributes more than 5.2% of net global greenhouse gas emissions. In Canada, healthcare activities are responsible for 4.6% of the country’s total greenhouse gas emissions, placing Canada’s healthcare system among the top four emitters per capita by country. In terms of the contributions of various healthcare sectors to greenhouse gas emissions, England’s National Health Service (NHS) demonstrated that anesthetic gases and metered dose inhalers (MDIs) play a significant role; MDIs alone contribute to 3.1% of the NHS’s total healthcare emissions. MDIs rely on hydrofluorocarbon (HFC) propellants to deliver medication, which act as potent greenhouse gases when released into the atmosphere. Depending on the formulation, one aerosol inhaler can have a carbon footprint equivalent to driving a gasoline-powered car up to 170 km. In Canada, short-acting beta-agonist (SABA) inhalers constitute 71% of total inhaler use, the majority of which are delivered via MDI devices. For these reasons, strategies that reduce the use of MDIs have the potential to reduce negative environmental consequences of respiratory care.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.030 |
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".