Digital Health in Canadian Schools of Nursing—Part B: Academic Nurse Administrators’ Perspectives
Bibliographic record
Abstract
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 programs and educators are at the forefront of this change, and are key to ensuring successful integration of digital health and informatics in nursing education and practice. 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. The purpose of this research was 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 representing the academic nurse administrators’ perspectives; nurse educator findings have been published separately (AUTHOR, 2020). Administrator respondents represented fewer than a third of Canadian schools of nursing, however findings indicate an appreciation of the importance of including digital health and informatics content in undergraduate curricula. There is some awareness of both CASN’s entry to practice informatics competencies and other related resources. Findings also suggest a willingness to provide the support needed for nurse educators to effectively address curricular integration. There was some difference of opinion when comparing educator and administrator perspectives. Variation was most evident when considering progress achieved to date. Findings also suggest administrators play a key role in assisting educators in overcoming barriers and advancing their informatics capacity to teach core digital health content. Digital heath integration is largely incumbent upon the leadership within schools of nursing as they are ideally positioned to provide the necessary vision and support. Some recommended tactics to address curricular integration are provided.
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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 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".