Examining how Ethics in Relation to Health Technology is Described in the Research Literature: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Given the increased use of technology in health care, both in extent and application, the importance of understanding the ethical implications of new health technologies increases. Profound insight into the possible ethical implications of new health technologies enhances the research and development of such technologies and the likelihood of eventual successful implementation in clinical practice. OBJECTIVE: This study aimed to gain an understanding of how and if researchers focused on health technologies describe the actual or possible ethical aspects of their research findings. METHODS: An established framework for scoping reviews was used to guide the methodology. Studies published in PubMed over the last 10 years were included if they study or refer to ethics in relation to health technology as defined by established frameworks. In total, 14,532 articles were screened, 692 were retained for full-text evaluation, and 227 were included for data extraction. RESULTS: In total, 250 (80.9%, N=309) studies were conducted in North America and Europe; literature review studies were dominant. Most studies (52.9%, 120/227) had no direct reference to any of the 4 basic ethical principles: beneficence, nonmaleficence, autonomy, and justice. In cases where studies referenced ethical theory, consequentialism dominated. CONCLUSIONS: When research about technology and ethics is published, the predominant focus is on its intent rather than its actual effect on patients. This lack of insight is problematic considering the vast advancement of technology in which ethics cannot keep up with understanding and offer insights on addressing ethical issues. This finding has implications for practice, research, and education.
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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 | 0.136 | 0.400 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.047 | 0.040 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".