Thematic analysis of tools for health innovators and organisation leaders to develop digital health solutions fit for climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: While ethicists have largely underscored the risks raised by digital health solutions that operate with or without artificial intelligence (AI), limited research has addressed the need to also mitigate their environmental footprint and equip health innovators as well as organisation leaders to meet responsibility requirements that go beyond clinical safety, efficacy and ethics. Drawing on the Responsible Innovation in Health framework, this qualitative study asks: (1) what are the practice-oriented tools available for innovators to develop environmentally sustainable digital solutions and (2) how are organisation leaders supposed to support them in this endeavour? METHODS: Focusing on a subset of 34 tools identified through a comprehensive scoping review (health sciences, computer sciences, engineering and social sciences), our qualitative thematic analysis identifies and illustrates how two responsibility principles-environmental sustainability and organisational responsibility-are meant to be put in practice. RESULTS: Guidance to make environmentally sustainable digital solutions is found in 11 tools whereas organisational responsibility is described in 33 tools. The former tools focus on reducing energy and materials consumption as well as pollution and waste production. The latter tools highlight executive roles for data risk management, data ethics and AI ethics. Only four tools translate environmental sustainability issues into tangible organisational responsibilities. CONCLUSIONS: Recognising that key design and development decisions in the digital health industry are largely shaped by market considerations, this study indicates that significant work lies ahead for medical and organisation leaders to support the development of solutions fit for climate change.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it