Climate-conscious pharmacy practice: An exploratory study of community pharmacists in Ontario
Bibliographic record
Abstract
Background: Climate change remains a significant challenge to global health. The health care system is a major contributor of greenhouse gas (GHG) emissions. While pharmacists are aware of climate change as an important societal issue, there is little known about the level of implementation of climate-conscious practices in community pharmacies throughout Ontario. Methods: An interview-based system was developed to gather insights from pharmacists regarding their perspectives on climate change and identify strategies to involve them in sustainable practices. Convenience and snowball sampling techniques were used to recruit pharmacists. Interviews were conducted by phone or Zoom, and transcripts were developed and analyzed for common themes using a qualitative method. Results and discussion: Twenty-four community pharmacists were interviewed. Three overarching themes emerged: 1) there is a knowledge and awareness gap in the pharmacy profession; 2) before pharmacists can prioritize sustainable practices, they must first address more immediate concerns in the pharmacy; 3) sustainable practice integration requires employment and regulatory changes as relying solely or extensively on the good intentions of frontline pharmacists is insufficient. Conclusion: While pharmacists show concern about the environmental impacts of their work, sustainable practice integration is very limited in community pharmacies across Ontario. There are many challenges and barriers to address. This includes closing the knowledge and awareness gap by incorporating climate-related topics into pharmacy curricula and providing educational seminars and resources for practicing pharmacists. In addition, improving workflow, changing standard operating procedures at a corporate level and providing incentives for implementing eco-friendly practices are crucial steps to address time and financial limitations. To manage patient safety concerns, providing resources that consider climate considerations as a secondary objective, when clinically appropriate for the patient, is the right approach to engage pharmacists. Lastly, advocating for employment and regulatory changes will be necessary for large-scale, durable changes to pharmacy practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".