Professional regulation in the digital era: A qualitative case study of three professions in Ontario, Canada
Bibliographic record
Abstract
Technology is transforming service delivery and practice in many regulated professions, altering required skills, scopes of practice, and the organization of professional work. Professional regulators face considerable pressure to facilitate technology-enabled work while adapting to digital changes in their practices and procedures. However, our understanding of how regulators are responding to technology-driven risks and the impact of technology on regulatory policy is limited. To examine the impact of technology and digitalization on regulation, we conducted an exploratory case study of the regulatory bodies for nursing, law, and social work in Ontario, Canada. Data were collected over two phases. First, we collected documents from the regulators' websites and regulatory consortiums. Second, we conducted key informant interviews with two representatives from each regulator. Data were thematically analyzed to explore the impact of technological change on regulatory activities and policies and to compare how regulatory structure and field shape this impact. Five themes were identified in our analysis: balancing efficiency potential with risks of certain technological advances; the potential for improving regulation through data analytics; considering how to regulate a technologically competent workforce; recalibrating pandemic emergency measures involving technology; and contemplating the future of technology on regulatory policy and practice. Regulators face ongoing challenges with providing equity-based approaches to regulating virtual practice, ensuring practitioners are technologically competent, and leveraging regulatory data to inform decision-making. Policymakers and regulators across Canada and internationally should prioritize risk-balanced policies, guidelines, and practice standards to support professional practice in the digital era.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".