Perceptions and Intentions of Nursing Students Regarding Digital Health: A Cross-sectional Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The integration of digital health technologies (DHTs) in clinical practice is accelerating, creating a need for nursing students to develop digital competencies aligned with professional expectations. In Quebec, curricular reforms aim to enhance digital health literacy, but limited data exist on students' preparedness. OBJECTIVE To assess nursing students’ perceptions, self-reported competencies, and willingness to engage with DHTs across different academic years. METHODS A cross-sectional descriptive survey assessing self-reported digital health competencies, attitudes, perceived training coverage, and intentions was conducted using an online questionnaire administered through Qualtrics. Participants (N=136) were recruited from three cohorts: first-year (G1, n=58), second-year (G2, n=55), and third-year (G3, n=23) nursing students. Data were analyzed using descriptive statistics and ANOVA tests with post-hoc analyses performed via IBM SPSS (version 28). RESULTS Significant differences were observed among cohorts concerning digital competencies and access to digital tools. Compared to first-year students (G1), third-year students (G3) showed higher proficiency with electronic medical records (G3: M = 3.29, SD = 1.31 vs. G1: M = 2.59, SD = 1.32, p = 0.011), virtual reality (G3: M = 4.53, SD = 1.11 vs. G1: M = 2.90, SD = 1.44, p < 0.001), and clinical databases (G3: M = 4.59, SD = 1.00 vs. G1: M = 3.21, SD = 1.55, p < 0.001). Despite positive attitudes toward digital health technologies across all groups, the coverage of digital health training within curricula was consistently perceived as insufficient (mean=2.97/5). This underscored a substantial gap between institutional expectations and actual digital training across all cohorts. CONCLUSIONS This study highlights critical gaps in digital health training among nursing students, emphasizing the need for targeted curricular reforms, such as the one currently underway at the University of Montreal. These efforts represent a promising opportunity to better align educational content with the evolving demands of healthcare systems. Today, preparing students in digital competencies is no longer just advantageous but may soon become essential for the next generation of nurses to navigate and lead within technology-driven care environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".