Environmental considerations in health technology assessments performed by Canadian agencies
Bibliographic record
Abstract
OBJECTIVES: Globally, several health technology assessment (HTA) agencies have started to incorporate environmental considerations into their assessments, given healthcare systems' substantial environmental footprint. In Canada, two HTA agencies, the Canadian Drug Agency and the Institut national d'excellence en santé et en services sociaux, have announced measures to help mitigate healthcare's contribution to climate change. Our aim was to review reports from both agencies to identify those incorporating environmental considerations. METHODS: We retrieved reports published between 1 May 2023 and 1 December 2024 by the two agencies. RESULTS: We identifed 202 reports, of which eleven were included. These reports covered diverse technologies, with greenhouse gas emissions and waste production being the most frequently considered environmental dimensions. Parallel evaluation was the predominant method for integrating environmental considerations. We believe that the limited number of reports included may reflect the challenges of incorporating such considerations into HTAs. CONCLUSION: By addressing these challenges, HTA agencies could play a pivotal role in guiding decisions that align with environmental goals.
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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 | 0.074 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.030 | 0.047 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".