Emotional Touch Nursing Competencies Model of the Fourth Industrial Revolution: Instrument Validation Study
Bibliographic record
Abstract
BACKGROUND: The Fourth Industrial Revolution is transforming the health care sector through advanced technologies such as artificial intelligence, the Internet of Things, and big data, leading to new expectations for rapid and accurate treatment. While the integration of technology in nursing tasks is on the rise, there remains a critical need to balance technological efficiency with empathy and emotional connection. This study aims to develop and validate a competency model for emotional touch nursing that responds to the evolving demands of the changing health care environment. OBJECTIVE: The aims of our study are to develop an emotional touch nursing competencies model and to verify its reliability and validity. METHODS: A conceptual framework and construct factors were developed based on an extensive literature review and in-depth interviews with nurses. The potential competencies were confirmed by 20 experts, and preliminary questions were prepared. The final version of the scale was verified through exploratory factor analysis (n=255) and confirmatory factor analysis (n=256) to assess its validity and reliability. RESULTS: From the exploratory analysis, 8 factors and 38 items (client-centered collaborative practice, learning agility for nursing, nursing professional commitment, positive self-worth, compliance with ethics and roles, nursing practice competence, nurse-client relationship, and nursing sensitivity) were extracted. These items were verified through convergent and discriminant validity testing. The internal consistency reliability was acceptable (Cronbach α=0.95). CONCLUSIONS: The findings from this study confirmed that this scale has sufficient validity and reliability to measure emotional touch nursing competencies. It is expected to be used to build a knowledge and educational system for emotional touch nursing.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".