Nurses’ Intention to Integrate AI Into Their Practice: Survey Study in Canada
Bibliographic record
Abstract
Background: The integration of artificial intelligence (AI) into health care is set to revolutionize the sector, offering opportunities to enhance diagnostic accuracy, personalize treatment, and improve patient outcomes. However, little is known about nurses' readiness to integrate AI into their professional practice. Objective: This study aimed to identify the key factors influencing nurses' intention to integrate AI into their practice. Methods: An online survey was distributed to 3000 members of the professional order of nurses in Quebec, Canada. A total of 312 nurses participated, with 307 completing the full questionnaire. Data were analyzed using descriptive statistics and partial least squares structural equation modeling. Results: Nurses' beliefs about the role of AI and trust in AI were found to predict intention to integrate AI significantly. Facilitating conditions influenced beliefs and familiarity with AI, which in turn shaped perceptions of AI's impact. The model explained 65% of the variance in behavioral intention. Conclusions: The findings highlight the importance of enhancing nurses' familiarity with AI and fostering positive beliefs and attitudes to promote effective integration. Educational strategies targeting these beliefs can facilitate AI adoption in nursing.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".