Digital Leadership Scale for Clinical Nurses (DLS-CN) : Development and Validation of Instrument (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> The rapid advancement of digital technologies, combined with the evolving complexity of healthcare environments, has introduced a new paradigm in nursing practice. Clinical nurses are now required not only to deliver safe and effective patient care but also to demonstrate competencies in digital literacy and innovation. Among these emerging competencies, digital leadership has become a critical attribute—enabling nurses to lead digital transformation, ensure patient safety, enhance care quality, and support system-level change within healthcare organizations. Despite its increasing relevance, there is a notable absence of validated measurement tools tailored to assess digital leadership in clinical practice. </sec> <sec> <title>OBJECTIVE</title> This study aimed to develop and psychometrically validate a Digital Leadership Scale for Clinical Nurses (DLS-CN) to systematically evaluate the digital leadership of nurses working in clinical settings. </sec> <sec> <title>METHODS</title> The scale development process followed a rigorous multi-step procedure. Initial items were derived from previous qualitative research involving a literature review and in-depth interviews, complemented by an additional literature review in this study. The content validity of 38 preliminary items was evaluated by nine experts over two rounds. A pilot test was conducted with 30 nurses, followed by cognitive interviews with five nurses to refine item clarity and relevance. The final set of items was administered to 446 clinical nurses across various healthcare institutions. Data were randomly split for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Additional analyses were conducted to evaluate item discrimination, convergent validity, and internal consistency using IBM SPSS 25.0 and AMOS 23.0. </sec> <sec> <title>RESULTS</title> The finalized DLS-CN consists of 29 items grouped under four domains: (1) ability to utilize digital technology, (2) digital safety management, (3) digital collaboration mindset, and (4) organizational influence. These four factors explained 56.9% of the total variance. The scale showed strong internal consistency (Cronbach’s α = .95). Convergent validity was demonstrated through strong positive correlations with the Nursing Informatics Competency Scale (r = .82, p < .001) and the Self-Leadership Scale (r = .83, p < .001). </sec> <sec> <title>CONCLUSIONS</title> The DLS-CN is a valid and reliable instrument for measuring digital leadership among clinical nurses. It offers a practical tool for educators, administrators, and researchers to assess and enhance digital leadership capabilities—ultimately supporting the digital transformation of healthcare systems. </sec> <sec> <title>CLINICALTRIAL</title> <p/> </sec>
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".