Digital Health in Canadian Schools of Nursing Part A: Nurse Educators’ Perspectives
Bibliographic record
Abstract
Introduction: While much progress has been achieved in advancing nursing informatics capacity in Canada, more work is needed to keep pace with the 21st century technological revolution. Nursing education programs and nurse educators are at the forefront of this change, and are key to ensuring successful integration of digital health technologies in future nursing practice. Methods: In 2018, a mixed methods study was conducted including a survey of nursing school administrators and nurse educators, telephone interviews, and one focus group meeting to understand the current state of digital health and informatics content integration in nursing curricula within Canadian schools of nursing. In this paper, we report on findings pertinent to nurse educators’ perspectives; findings from the nurse administrator survey will be reported separately. Results: Congruent with the general literature, findings from this study suggest that the challenges for nurse educators to realize informatics integration in nursing curricula are universal. A developing awareness of CASN’s entry-to-practice informatics competencies and a strong interest and desire among nurse educators to respond to current demands for advancing the digital health learning needs of future nurses are evident. However, there are still gaps and challenges in digital health content integration that need to be addressed. Conclusion: Realizing the vision of adequately prepared nursing workforce for digital health requires a shift in thinking about the role of informatics in nursing education and practice, as well as concerted efforts by all stakeholders. In view of the current technological revolution impacting all sectors of society including health care, nurse educators are in a unique position to shape the future of nursing practice. Educator engagement and administrative leader support within every Canadian school of nursing are vital for overcoming barriers and advancing the informatics capacity of all future nurses.
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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 | 0.000 |
| 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".