Healthcare AI: A Revised Quebec Framework for Nursing Education
Bibliographic record
Abstract
Artificial intelligence health technologies (AIHT) are taking their place in the practice of nursing. However, the curricula have not evolved to include competencies required of nursing graduates to incorporate their impact on theory and practice. This project was born of an identified need by nurse educators to articulate new competencies grounded in the literature and expert knowledge. Based on extensive literature reviews and an iterative process of expert validation, this paper provides recommendations for five new competencies that will be needed for nurses to use AIHT responsibly, ethically, and intelligently in the best interests of patient care. The methodology started with a literature review, then expert validation, leading to the development of the proposed competency framework, and finally validation with experts in artificial intelligence (AI) and health care. The first two competencies proposed address the underlying theory needed for effective practice: 1) Students will be able to apply knowledge of informatics and digital health technology to the practice of nursing; and 2) Students will be able to apply their knowledge of AIHT and their inherent benefits and limitations. The subsequent three competencies address application in practice: 3) Students will be able to use AIHT safely and effectively within their nursing practice; 4) Students will be able to participate in the development of AIHT guidelines considering ethical, social, and legal implications; and 5) Students will be able to engage in the development of AIHT training to support continuing nurse education. Clear statements, achievement contexts, elements, and performance criteria are provided for all levels of post-secondary education in Quebec including RN, BScN, and graduate-level programs. The proposed framework would also be of interest to nurse educators across Canada and internationally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".