Double burden or single duty to care? Health innovators’ perspectives on environmental considerations in health innovation design
Bibliographic record
Abstract
Objectives The healthcare sector lags behind other industries in efforts to reduce its environmental footprint. This study aims to better understand how those who design new health technologies (devices, technical aids and information technologies) perceive and address environmental considerations in their practice. Methods We conducted in-depth interviews with engineers, industrial designers, entrepreneurs and clinicians (n=31) involved in the design, development and distribution of health innovations in Quebec and Ontario (Canada). A qualitative thematic data analysis identified similarities and variations across respondents’ viewpoints. Results Innovators’ views emphasise the following: (1) the double burden of supporting patient care and reducing the environmental impact of healthcare; (2) systemic challenges to integrating environmental considerations in health innovation design, development and use and (3) solutions to foster the development of environmental-friendly health innovations. Although innovators tend to prioritise patient care over the environment, they also call for public policies that can transform these two imperatives into a single duty to care. Conclusions Health innovators are uniquely positioned to tackle challenges and develop creative solutions. Policymakers and regulators should, however, actively steer the healthcare industry towards a more sustainable modus operandi by giving full attention to environmental considerations in health innovation design.
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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 | 0.050 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.041 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".