Attitude, Knowledge, and Practice on Evidence‐Based Nursing among Registered Nurses in Traditional Chinese Medicine Hospitals: A Multiple Center Cross‐Sectional Survey in China
Bibliographic record
Abstract
Objective. This study was to describe RNs' attitude, knowledge, and practice on evidence-based practice (EBP) in traditional Chinese nursing field and to estimate the related sociodemographic and professional factors. Methods. A multiple institutional cross-sectional survey design with self-reported EBP Questionnaire (EBPQ) and self-designed questionnaires were used. Results. The average scores of the total EBPQ were with a mean of 4.24 (SD = 0.79). The score of attitude was the highest one, followed by the knowledge score, and the lowest one is practice. RNs with longer experience reported stronger EBP knowledge (H = 6.64, P < 0.05). And RNs under higher working pressure reported less positive attitudes (ρ = 0.17, P < 0.001), whereas RNs holding negative professional attitude reported lower scores (Spearman's ρ: 0.12 to 0.15, P < 0.001). Significant statistics were found between RNs with research experience and without in attitude (t = -2.40, P < 0.05) and knowledge (t = -2.43, P < 0.05). Conclusions. Respondents generally viewed EBP positively and their attitudes towards EBP tended to be more positive than knowledge and practice of EBP. Data also showed that longer working experience, having administrative position, research experience, lighter working load, and better professional attitude might facilitate EBP.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".