RNAO’s Artificial Intelligence Innovations: A Novel Strategy to Advance Evidence-Based Nursing Practice
Bibliographic record
Abstract
Introduction. Artificial intelligence and machine learning methodologies, such as prediction, pattern recognition, or general inference based on the data used in clinical aspects, must fit within the intended purposes of developing it. This article aims to provide high-level, non-technical details of the initiative and a comprehensive approach that has been taken to integrate AI-powered techniques in evidence-based nursing practices appropriately. Methodology. A multi-pronged phased approach was considered for developing artificial intelligence tools. This approach includes conducting a scoping review, analyzing data to identify patterns of impactful intervention, employing data triangulation, enhancing data collection based on impactful intervention strategies, and developing a prototype (pilot) for an artificial intelligence tool. The process encompasses piloting, testing and training, validation, and implementation. Results. In this early stage of piloting the tool, the primary focus was identifying patterns from various information gathered from healthcare organizations. This analysis revealed opportunities for knowledge generation, facilitated the expedited implementation of guidelines, and enhanced resource efficiency. Discussion. Focusing on a data-driven model to inform best practices for implementing guidelines and identifying the most impactful interventions is facilitated by extensive in-house data storage. The triangulation of approaches to guideline development, implementation, and evaluation contributes to developing this scientifically validated artificial intelligence and machine learning initiative. Conclusion. Any artificial intelligence technique requires extensive data. To provide healthcare organizations with the best available evidence, purposeful efforts must be made to structure data collection and ensure data quality before expanding the development of artificial intelligence tools.
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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 | 0.085 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".