Clearing the air: a systematic review on leadership challenges with sustainable inhaler prescribing
Bibliographic record
Abstract
BACKGROUND: The environmental impact of inhalers, particularly pressurised metered dose inhalers with high global warming potential, poses significant challenges in the context of planetary health. Although dry powder inhalers (DPIs) offer a more sustainable alternative, entrenched prescribing practices prevail. This systematic review evaluates patient and physician perspectives on inhaler environmental impacts and examines barriers and opportunities for leadership in adopting sustainable practices. METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive literature search was performed from inception to 12 June 2024, across Medline via EBSCO, EMBASE via Elsevier and Web of Science. Four studies were included, surveying 433 participants. Data extraction and risk-of-bias assessment were conducted using a standardised form and the Newcastle-Ottawa Scale. RESULTS: Findings indicate that while both patients and providers express environmental concerns, limited awareness and entrenched clinical practices hamper the transition to DPIs. Leadership insights reveal that a fragmented sense of responsibility, insufficient training and low confidence in discussing environmental impacts are significant barriers. However, targeted education and interprofessional collaboration have been shown to increase the willingness to adopt sustainable inhaler practices. CONCLUSIONS: The results underscore the need for leadership in healthcare to champion sustainable prescribing. Empowering clinicians through education, clear clinical guidelines and eco-ethical leadership initiatives is essential. Health leaders have the opportunity to transform practice by integrating environmental considerations into routine care, ultimately advancing planetary health. THE PROSPERO REGISTRATION NUMBER: CRD42024552555.
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 | 0.024 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".