Informatics Competency and Technology Self-Efficacy Profiles in Saudi Undergraduate Nursing Students: A Cross-Sectional Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The Saudi Arabian healthcare sector is transforming under Vision 2030, with the goal of digitizing services. This necessitates a digitally prepared nursing workforce; however, evidence suggests that nursing students have limited informatics competency, and these skills are minimally covered in their training OBJECTIVE To measure the baseline informatics competency and technology self-efficacy of Saudi undergraduate nursing students METHODS Using a descriptive cross-sectional design, data were collected from 243 undergraduate nursing students from Hail University via an online survey. The survey content covered demographics, informatics competency (Canadian Nurse Informatics Competency Assessment Scale), and digital technology self-efficacy. Data analysis employed descriptive statistics, t-tests, analysis of variance, and hierarchical multiple regression analysis RESULTS Students reported a moderate level of informatics competency, with a mean Canadian Nurse Informatics Competency Assessment Scale score of 2.16 (out of 4). They also showed moderate-to-high self-efficacy for digital technology, with a mean score of 2.7 (out of 4). Competency informatics scores were significantly higher among students with prior informatics training and frequent electronic health record exposure. Additionally, self-efficacy for digital technology was positively associated with informatics competency CONCLUSIONS There is a substantial gap between the informatics competencies of Saudi undergraduate nursing students and the expectations of Vision 2030. The findings indicate the need for improvements in informatics training and clinical electronic health record experience in the nursing curriculum to create a digitally competent workforce in the future CLINICALTRIAL NA
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".