Development of a Self-Assessment Audit Instrument to Support Climate-Conscious Community Pharmacy Practice and Education
Bibliographic record
Abstract
Climate breakdown continues to occur at an alarming pace, and the need for all citizens and professionals to respond has never been so urgent. Healthcare work contributes significantly to greenhouse gas (GHG) emissions, representing an opportunity and responsibility for pharmacists to engage in more climate-conscious practices. A key informant interview-based qualitative research method was undertaken to identify options and strategies for community pharmacists to contribute positively to achieving climate-related carbon footprint reduction goals. A total of 10 thought leaders in climate-conscious pharmacy practice were interviewed using a semi-structured protocol, and a constant comparative data analysis method was used to identify common themes and priorities. A self-assessment audit instrument (SAAI) was identified as a positive first step to build awareness, knowledge, skills, and confidence amongst community pharmacists regarding actionable climate-conscious community pharmacy practice. The SAAI supported both self-reflection and self-assessment, while signposting additional resources that could be accessed by pharmacists to continue their learning and professional development. Further work in this area is required if community pharmacy is to contribute positively to climate reduction targets locally and globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".