Sustaining Knowledge Transfer Through Leadership
Bibliographic record
Abstract
ABSTRACT The delivery of efficient and high quality nursing care in today's health care environment requires that nurses utilize research evidence to guide their decision‐making in clinical practice. Best Practice Guidelines (BPGs) are an accessible link to research evidence and an effective tool for improving quality health care. Despite reported benefits and dissemination strategies, the transfer and uptake of guidelines does not always occur, and sustainability over time is seldom evaluated. Emerging evidence indicates that leadership plays a fundamental role in the transfer and sustained use of BPGs in clinical decision‐making. However, a clear understanding of effective nursing leadership to sustain knowledge transfer remains unclear and research is limited. Objective: To describe the evolving development of a conceptual framework for sustaining knowledge transfer of BPGs into nursing practice in Canadian health care settings. Methods: A critical review of the literature has been conducted. A model was developed to direct an inquiry about leadership characteristics and activities in 10 organizations that either sustained, partially sustained, or did not sustain practice guidelines 2 years after a pilot implementation initiative administrated by the Registered Nurses Association of Ontario Best Practice Guideline (RNAO‐BPG) Project. Findings: A conceptual framework has been developed to represent the key components of leadership and how these components are operationalized for knowledge transfer of BPGs into clinical practice. The model is an assimilation of the United Kingdom and North American literature and includes a pathway for personal attributes, defined skills, and knowledge repertoires that emerge from two domains of influence: cultural/clinical and political/organizational. Although the literature shows many similarities between leadership paradigms, some differences in discourse and approach were found. Implications: To create successful strategies to sustain practice innovations, we need to better understand how nursing leaders operate effectively within both clinical and organizational environments. This framework is the groundwork for conceptualizing nursing leadership as a vehicle to influence, shape, and sustain evidence‐based nursing practice.
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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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".