Differences in evidence‐based nursing practice competencies of clinical and academic nurses in China and opportunities for complementary collaborations: A cross‐sectional study
Bibliographic record
Abstract
Abstract Aims and objectives To explore the evidence‐based nursing practice (EBNP) competencies of clinical and academic nurses and their collaboration needs for supporting EBNP. Background Academic‐practice partnerships have strong potential to overcome the key barriers to EBNP. However, there is little known about the collaboration needs of clinical and academic nurses for EBNP. Design A cross‐sectional study. Methods We recruited clinical and academic nurses online during November 2021 to January 2022. Using a reliable and validated scale and adapted questionnaires, data were collected relating to demographic information, EBNP‐related resources availability, EBNP competencies and EBNP collaboration needs. These data were described using descriptive statistical methods. The t test, χ2 test and Mann–Whitney U test were used to evaluate if the different responses between clinical and academic nurses were statistically significant. This study was reported following the STROBET checklist. Results Two 240 clinical nurses and 232 academic nurses submitted questionnaires. There was no difference in overall EBNP competence between clinical and academic nurses. However, clinical nurses reported lower levels of competence and stronger intentions to collaborate with academic nurses when searching for, appraising, and synthesising evidence. Academic nurses reported lower levels of competence and stronger intentions to collaborate with clinical nurses for disseminating and implementing evidence. Conclusion Clinical and academic nurses both reported high needs for collaborating to overcome their perceived role limitations. Clinical and academic nurses have different strengths and limitations in EBNP. These role differences and intentions to collaborate for different dimensions of EBNP competence suggest that clinical and academic nursing roles could be complementary to each other, offering opportunities for synergistic collaborations to better support overall EBNP. Relevance to clinical practice Healthcare and academic institutions should promote academic‐practice partnerships as opportunities to gain complementary expertise on different dimensions of EBNP, and to improve nurses' competencies and confidence in EBNP overall.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".