ENVIRONMENTAL IMPACT ASSESSMENT OF A HEALTH TECHNOLOGY: A SCOPING REVIEW
Bibliographic record
Abstract
INTRODUCTION: The Health Technology Expert Review Panel is an advisory body to Canadian Agency for Drugs and Technologies in Health (CADTH) that develops recommendations on health technology assessments (HTAs) for nondrug health technologies using a deliberative framework. The framework spans several domains, including the environmental impact of the health technology(ies). Our research objective was to identify articles on frameworks, methods or case studies on the environmental impact assessment of health technologies. METHODS: A literature search in major databases and a focused gray literature search were conducted. The main search concepts were HTA and environmental impact/sustainability. Eligible articles were those that described a conceptual framework or methods used to conduct an environmental assessment of health technologies, and case studies on the application of an environmental assessment. RESULTS: From the 1,710 citations identified, thirteen publications were included. Two articles presented a framework to incorporate environmental assessment in HTAs. Other approaches described weight of evidence practices and comprehensive and integrated environmental impact assessments. Central themes derived include transparency and repeatability, integration of components in a framework or of evidence into a single outcome, data availability to ensure the accuracy of findings, and familiarity with the approach used. CONCLUSIONS: Each framework and methods presented have different foci related to the ecosystem, health economics, or engineering practices. Their descriptions suggested transparency, repeatability, and the integration of components or of evidence into a single outcome as their main strengths. Our review is an initial step of a larger initiative by CADTH to develop the methods and processes to address the environmental impact question in an HTA.
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 | 0.032 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".