Regulating professional ethics in a context of technological change
Bibliographic record
Abstract
BACKGROUND: Technological change is impacting the work of health professionals, especially with recent developments in artificial intelligence. Research has raised many ethical considerations respecting clinical applications of artificial intelligence, and it has identified a role for professional regulation in helping to guide practitioners in the ethical use of technology; however, regulation in this area has been slow to develop. This study seeks to identify the challenges that health professionals face in the context of technological change, and whether regulators' codes of ethics and guidance are sufficient to help workers navigate these changes. METHODS: We conducted mixed methods research in Ontario, Canada, using qualitative content analysis of regulators' codes of ethics and practice guidance (26 regulators, 63 documents analysed), interviews with 7 representatives from 5 health profession regulatory bodies, and focus groups with 17 healthcare practitioners across 5 professions in the province. We used thematic analysis to analyse the data and answer our core research questions. RESULTS: We find that codes of ethics focus more on general principles and managing practitioners' relationships with clients/patients; hence, it is not clear that these documents can successfully guide professional practice in a context of rapid technological change. Practitioners and regulatory body staff express ambivalence and uncertainty about regulators' roles in regulating technology use. In some instances, health professionals experience conflict between the expectations of their regulator and their employer. These gaps and conflicts leave some professionals uncertain about how to practice ethically in a digital age. CONCLUSIONS: There is a need for more guidance and regulation in this area, not only for practitioners, but with respect to the application of technology within the environments in which health professionals work.
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How this classification was reachedexpand
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 | 0.006 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".