Teaching Informatics Competencies within Nursing Education: A Scoping Review of Teaching Strategies
Bibliographic record
Abstract
As healthcare delivery becomes increasingly digital, nursing informatics knowledge has become essential for nurses to participate effectively in digitally enabled healthcare environments. The Canadian Association of Schools of Nursing (CASN) released updated nursing informatics entry-to-practice competencies in 2025 to inform nursing education in Canada. However, the integration of informatics knowledge and content into nursing curricula and professional practice education remains inconsistent. Mapping the teaching strategies used by nursing educators to support these competencies is a necessary step toward understanding current educational practices.To identify teaching strategies currently employed to support the development of nursing informatics knowledge and competencies in undergraduate, graduate, and professional nursing education.The search strategy included six electronic databases: Scopus, Web of Science, CINAHL, ERIC, Embase, and Medline. Key search terms were synonyms and combinations of "informatics competencies," "nursing informatics," and "education." Articles were included if they specifically described nursing informatics competencies and how they were taught to nursing students and practicing nurses. The papers were independently reviewed by two reviewers, and a thematic analysis was conducted to identify teaching strategies.A total of 120 publications were included in the scoping review. Seven strategies to teach nursing informatics were identified: (1) integration of electronic health records into laboratory simulations; (2) integration of informatics competency frameworks; (3) accessing online educational resources; (4) integration of mobile technologies; (5) informatics- competent educators; (6) integrating patient safety and data ethics; and (7) interdisciplinary collaboration.There is an urgent need to align nursing education with the rapid rise of technologies to prepare nurses for safe, competent, and person-centered digital care. This review highlights diverse, CASN-aligned teaching strategies that support informatics competency development across all levels of nursing education. The findings offer practical guidance for educators and inform cirriculum planning and professional practice education in digitally-enabled environments.
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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 | 0.046 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.038 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".