The effect of implementation leadership training for nursing informal leaders in the evidence-based practice
Bibliographic record
Abstract
Background Implementation leadership is important for the successful implementation of evidence-based practice (EBP). Informal leaders, who are important promoters of EBP in nursing in the current healthcare system, can affect nursing management, organizational effectiveness, and cultural quality positively. However, informal leaders may lack training in leadership management and EBP. This study evaluated the effectiveness of the training program for implementation leadership, aiming to improve the leadership of informal leaders in EBP. Methods Based on the Ottawa Model, this study designed a training program for implementation leadership, which lasted for 4 months and had 60 class hours. Seventy-five nursing informal leaders were trained in three steps of “Theoretical Training-Interactive Workshop-Theme Report”. Before the first training and after the last training, we evaluated the training effectiveness using Kirkpatrick’s evaluation model at the four levels of reaction (training satisfaction survey), learning (Evidence-Based Practice Belief Scale), behavior (Evidence-Based Nursing Competence Scale), and result (Implementation Leadership Scale). Data were analyzed by descriptive statistics, and paired t-tests for effect sizes. Results At the reaction level, the informal leaders had 100% engagement and high satisfaction score. At the learning level, the score of the Evidence-Based Practice Belief Scale of informal leaders after training [(69.24 ± 5.32)] vs. [(59.91 ± 5.96)] was significantly higher than that before training (p < 0.001). At the behavior level, the score of the Evidence-Based Nursing Competence Scale of informal leaders after training [(74.47 ± 5.75) vs. [(56.37 ± 7.15)] was significantly higher than that before training (p < 0.001). At the result level, the score of the Implementation Leadership Scale of informal leaders after training [(38.88 ± 2.76) vs. [(30.01 ± 3.24)] was significantly higher than that before training (p < 0.001). Conclusion The training program is a highly accepted, practical, and effective implementation strategy for informal leaders, which can improve the evidence-based nursing belief, evidence-based nursing competence, and implementation leadership of nursing informal leaders, and has a positive impact on 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".