The Sociotechnical Ethics of Digital Health: A Critique and Extension of Approaches From Bioethics
Bibliographic record
Abstract
The widespread adoption of digital technologies raises important ethical issues in health care and public health. In our view, understanding these ethical issues demands a perspective that looks beyond the technology itself to include the sociotechnical system in which it is situated. In this sense, a sociotechnical system refers to the broader collection of material devices, interpersonal relationships, organizational policies, corporate contracts, and government regulations that shape the ways in which digital health technologies are adopted and used. Bioethical approaches to the assessment of digital health technologies are typically confined to ethical issues raised by features of the technology itself. We suggest that an ethical perspective confined to functions of the technology is insufficient to assess the broader impact of the adoption of technologies on the care environment and the broader health-related ecosystem of which it is a part. In this paper we review existing approaches to the bioethics of digital health, and draw on concepts from design ethics and science & technology studies (STS) to critique a narrow view of the bioethics of digital health. We then describe the sociotechnical system produced by digital health technologies when adopted in health care environments, and outline the various considerations that demand attention for a comprehensive ethical analysis of digital health technologies in this broad perspective. We conclude by outlining the importance of social justice for ethical analysis from a sociotechnical perspective.
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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 | 0.054 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.180 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".