Psychometric Testing of the Evidence-Based Practice Nursing Leadership Scale and the Work Environment Scale After Cross-Cultural Adaptation in Mainland China
Bibliographic record
Abstract
Implementation and sustainability of the evidence-based practice (EBP) approach within systems of health-care delivery require leadership and organizational support, yet few instruments have been developed specifically in Mainland China. The purpose of this study was to adapt the EBP Nursing Leadership Scale and the EBP Work Environment Scale to Mainland China's cultural context and to evaluate the psychometric properties of the newly adapted Chinese version. A pilot study was conducted in Mainland China with 25 clinical nurses. A subsequent validation study was conducted with 419 nurses from Mainland China. A content validity index of .985 and .982 was achieved. The split-half coefficient was .890 for the EBP Nursing Leadership Scale and .892 for the EBP Work Environment Scale. Test-retest reliability was .871 and .855, respectively. Principal component analysis resulted in a one-factor structure explaining 62.069% of the total variance for the EBP Nursing Leadership Scale and 62.242% of the total variance for the EBP Work Environment Scale. Both of the newly cross-culturally adapted scales possess adequate internal consistency and test-retest reliability and validity and therefore may be utilized in health-care environments to assess leadership and organizational support for EBP in Mainland China.
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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 | 0.005 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".