Do clinical practice guidelines follow sustainable healthcare principles? A review of respiratory guidance
Bibliographic record
Abstract
Background Respiratory care is an important site for climate action, given that common treatments for conditions such as asthma and COPD produce significant greenhouse gas emissions, even as respiratory health is negatively impacted by climate change. Clinical guidelines provide key information for healthcare professionals and can promote approaches to healthcare that can mitigate negative environmental impacts, and optimise patient treatment, care delivery, and equitable outcomes, and bring awareness and legitimacy to sustainable healthcare practices. Methods Twenty national and international clinical respiratory guidelines were purposively selected and screened for inclusion of four principles of sustainable clinical practice: prevention, patient empowerment and self-care, lean service delivery, and low carbon alternatives. A screening framework specific to respiratory care implications was developed and used to review each guideline for mention of relevant topics, recommendations, and explicit links to sustainability in relation to each principle. Findings Sustainable clinical care principles were evident in most guidelines reviewed, environmental sustainability was mentioned infrequently. Many guidelines emphasised prevention (more secondary than primary) and support for patient preference and streamlining care, yet there was rarely mention of how these recommendations could contribute to lowering the environmental impacts of health systems. Low carbon alternatives were mentioned in only three guidelines. Conclusions While many clinical respiratory guidelines make recommendations in accordance with the principles of prevention, patient empowerment and self-care, and lean service delivery, reducing the carbon footprint of healthcare was rarely mentioned in the guidelines. Including explicit attention to the environmental impact of clinical care in guidance could support efforts to reduce the wider harms of healthcare, meanwhile, noting the clinical benefits of sustainable approaches could promote the uptake of recommendations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".