Canadian Association of Radiologists Statement on Planetary Health Education in Radiology
Bibliographic record
Abstract
The health of Canadians is already impacted by climate change due to wildfire smoke, heat domes, floods, droughts, and the changing distribution of vector borne disease. The healthcare sector contributes to climate change, accounting for approximately 4.6% of annual greenhouse gas emissions in Canada. Healthcare teams have a responsibility and opportunity to reduce harm by limiting emissions and waste, and engaging the public in understanding the planetary health links between clean air and water, a stable climate, a healthy planet and human health. Transformation of Canadian healthcare to a low carbon, climate resilient system will be enhanced by physician engagement and leadership. Cornerstones to physician participation include knowledge of the anthropogenic etiology of the climate crisis, the human health impacts, and the contribution providing healthcare makes to the climate crisis. Integration of climate change knowledge into the Canadian Radiology educational curricula is essential to position radiologists to lead transformative change in mitigation and adaptation of the healthcare system to the climate crisis. This statement is intended to provide guidelines to optimize education and research for current and future Canadian radiologists, and builds on existing planetary healthcare education publications and the Canadian Association of Radiologists Statement on Environmental Sustainability in Medical Imaging.
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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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".