Australian and Canadian clinicians’ views and application of ‘carbon health literacy’: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Clinical care contributes to at least 50% of the greenhouse gas (GHG) emissions of healthcare. This includes the 40% of healthcare that is harmful or low value, adding avoidable emissions without improving health or quality of care. Clinicians are well-placed to mitigate emissions associated with the provision of clinical care. This study aimed to explore clinicians' views on a new construct we have termed 'carbon health literacy' to understand how knowledge, skills and capacities related to the emissions of clinical care has application in clinical practice. METHODS: Qualitative interviews were conducted between August 2022 and February 2023 with clinicians from Australia (n = 15) and Canada (n = 13). Clinicians with an interest in climate change and healthcare sustainability were sampled from a variety of clinical specialty areas, such as primary care, nursing, anaesthetics, and emergency. Clinicians were recruited through advertising on social media and via professional networks. A pre-piloted interview schedule was used to guide the interviews. Interviews were audio recorded, transcribed verbatim and analysed using framework analysis. RESULTS: Participants viewed carbon health literacy as an increasingly important skill for clinicians to have or acquire, though they reported that the level of carbon health literacy and knowledge needed varies by job roles, clinical specialty areas, and individual capacity to generate healthcare system change. Many clinicians reported implementing strategies to mitigate their work-related GHG emissions, such as reducing waste or choosing lower carbon commuting options. There was limited awareness of reducing low-value care as a strategy to decrease emissions. All participants had encountered barriers to providing low-carbon care, including managing patient expectations, inadequate training and information, and limited capacity to generate system change in their organisational roles. CONCLUSIONS: To support the delivery of high value low carbon healthcare, work is needed to build the carbon health literacy of clinicians and remove other barriers currently impeding their capacity to practice and promote sustainable clinical care.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".