Reducing the adverse environmental impacts of prescribing
Bibliographic record
Abstract
Background: The climate crisis makes it essential that clinicians consider the environmental impacts of prescribing. Canada’s healthcare system contributes 4.6% of total greenhouse gas (GHG) emissions, of which 1.2% are from drugs. Pharmaceutical production, packaging, and disposal of medications contribute to pollution and GHG emissions. Pressurized metered dose inhalers (pMDIs) are especially harmful due to their hydrofluorocarbon propellants. The overuse of medications further compounds these impacts. Aims: This Therapeutics Letter highlights the environmental impacts of prescribing and offers strategies for reducing these impacts. It emphasizes the importance of deprescribing, conservative prescribing, and the role played by patient education and involvement in medication choices. Recommendations: Clinicians should consider environmental impacts when prescribing and choose lower impact or non-pharmacologic therapies when possible. Employ conservative prescribing, review medications regularly and deprescribe where appropriate. Reduce the overprescribing of proton pump inhibitors and antibiotics and consider ways of minimizing packaging waste. Switch patients from pMDIs to dry powder inhalers to reduce GHG emissions. Educating patients on environmental impacts and sustainable practices such as medication disposal to further mitigate 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".