Digital Leadership Scale for Clinical Nurses (DLS-CN) : Development and Validation of Instrument (Preprint)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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>
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it