The Role of Nursing Best Practice Champions in Diffusing Practice Guidelines: A Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: While the importance of nursing best practice champions has been widely promoted in the diffusion of evidence-based practice, there has been little research about their role. By learning more about what champions do in guideline diffusion, the nursing profession can more proactively manage and facilitate the role of champions while capitalizing on their potential to be effective leaders of the health care system. AIM: To determine how nursing best practice champions influence the diffusion of Best Practice Guideline recommendations. METHODS: A mixed method sequential triangulation design was used involving two phases: (1) key informant interviews with 23 champions between February and July 2006 and (2) a survey of champions (N= 191) and administrators (N= 41) from September to October 2007. Qualitative findings informed the development of surveys and were used in interpreting quantitative information collected in phase 2. RESULTS: Most interview and survey participants were female, employed full-time, and had worked in practice for over 20 years. Qualitative and quantitative findings suggest that champions influence the use of Best Practice Guideline recommendations most readily through: (1) dissemination of information about clinical practice guidelines, specifically through education and mentoring; (2) being persuasive practice leaders at interdisciplinary committees; and (3) tailoring the guideline implementation strategies to the organizational context. CONCLUSIONS AND IMPLICATIONS: Our research suggests that nursing best practice champions have a multidimensional role that is well suited to navigating the complexities of a dynamic health system to create positive change. Understanding of this role can help service organizations and the nursing profession more fully capitalize on the potential of champions to influence and implement evidence-based practices to advance positive patient, organizational, and system outcomes.
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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 | 0.062 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".