Development of a training program prototype to enhance implementation leadership competencies and behaviours of Chinese unit nurse managers: a qualitative descriptive study
Bibliographic record
Abstract
Abstract Background Unit nurse managers hold essential positions that can facilitate implementation of evidence-based practice. Studies showed that nurse managers in China lacked competencies and behaviours necessary to lead evidence-based practice implementation. The aim of the current study was to develop a context-fit training program prototype to enhance leadership competencies and behaviours regarding evidence-based practice implementation of Chinese unit nurse managers. Method We used a descriptive qualitative study design and followed the integrated knowledge translation approach to co-develop the prototype in a tertiary hospital in Changsha, China. Seven nurse managers from the participated hospital and a researcher co-developed the prototype based on the Ottawa Model of Implementation Leadership (O-MILe). The development process encompassed four phases from November 2021 to March 2022 that involved group discussions (n = 4) and individual interviews (n = 21). All data were analysed by two independent researchers using the thematic analysis method. Results Managers agreed that all O-MILe behaviours were important to evidence-based practice implementation, and only minor modifications were needed for clarification and adaptation. The actions managers identified that could operationalize the leadership behaviours were related to current clinical practices, evidence-based practice, nurses, patients, interprofessional staff members, incentives and resources, organization and external entities. Three types of general competencies related to evidence-based practice, professional nursing, and implementation leadership were identified. Multimodal activities such as lectures, experience sharing, group discussions, plan development and coaching were suggested to deliver the training program. Conclusions All O-MILe leadership behaviours were perceived as essential for unit nurse managers to lead EBP implementation in the hospital context in China. We identified the leadership actions and the competencies required for nursing managers to implement EBP in China. Further studies are required to evaluate the acceptability and impact of this prototype. Further studies with large sample sizes across various clinical settings are needed to facilitate the generalization of the findings and gain an in-depth understanding of the program.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".