Conceptual and Governance Gaps in Nursing Regulatory Guidance
Bibliographic record
Abstract
As Artificial Intelligence (AI) systems become embedded in healthcare delivery, nursing regulatory bodies face unprecedented challenges in developing guidance that protects public safety while enabling appropriate technology use. Our analysis examines a recently published guideline, identifying definitional inadequacies, situating AI within an outdated, anthropocentric paradigm that diverges substantially from contemporary AI scholarship and fails to capture how modern systems actually function. Our analysis also identifies well as three critical gaps between regulatory frameworks and contemporary understanding of AI systems. First, its privacy considerations treat AI as conventional healthcare technology, missing the AI-specific characteristics which are distinct from more traditional healthcare risks. Second, the organization inappropriately transfers complex technical assessment responsibilities from organizations to individual practitioners. Third, while the organization acknowledges algorithmic bias affecting equity-deserving populations, the guidelines do not acknowledge the operational frameworks which are required for bias detection and remediation, again placing technical assessment burdens on individual nurses. To effectively address the implications of AI with regards to nursing regulation, interdisciplinary collaboration, clear organizational accountability frameworks, and evidence-based guidance reflecting technical realities are required. This analysis identifies priority areas for nursing regulatory bodies as they develop or revise AI practice guidance.
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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 | 0.130 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.015 | 0.014 |
| 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".