Eco-Sustainability in Hospital Pharmacy: A Pilot Survey on ‘Going Green’
Bibliographic record
Abstract
Purpose: Between 2009 and 2015, the Canadian health care system was estimated to be responsible for 4.6% of national carbon emissions. Determine awareness of and describe eco-initiatives that the department of pharmacy can implement to aim to reduce the carbon footprint in hospital pharmacy in an effort to ‘go green’. Methods: In a quality improvement initiative, pharmacy employees (i.e. pharmacists and pharmacy technicians) completed a cross-sectional survey designed to gauge willingness to ‘go green’ at work, to identify actionable areas of waste, and to assess commuting practices. Results: A total of 15 respondents completed the survey conducted March 14th –April 7th, 2022. Most respondents (73%) were willing to engage in more sustainable practices at work. The main barriers to implementing green practices at work were ‘too time consuming’ (20%), ‘adds too much complexity’ (20%), and ‘cost’ (16%). For commuting, 60% indicated the primary mode of transportation as ‘personal vehicle’, where ‘subsidized transit’ and was listed as the greatest incentive that could encourage a greener commute. The three largest areas of waste cited were ‘single use plastic’ (36%), ‘limited of awareness of green practices’ (15%), and ‘lights left on in empty rooms’ (12%). Conclusions: Pharmacy staff shared willingness to engage in more sustainable ‘go green’ practices but raised challenges to do so. With the knowledge that Canada has the second most climate intensive health system, there is a need for future research to describe how hospital pharmacies can contribute strategically to ‘go green’, advancing with implementing low carbon sustainable pharmacy practices.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".