How nursing leaders promote evidence‐based practice implementation at point‐of‐care: A four‐country exploratory study
Bibliographic record
Abstract
AIMS: To describe strategies nursing leaders use to promote evidence-based practice implementation at point-of-care using data from health systems in Australia, Canada, England and Sweden. DESIGN: A descriptive, exploratory case-study design based on individual interviews using deductive and inductive thematic analysis and interpretation. METHODS: Fifty-five nursing leaders from Australia, Canada, England and Sweden were recruited to participate in the study. Data were collected between September 2015 and April 2016. RESULTS: Nursing leaders both in formal managerial roles and enabling roles across four country jurisdictions used similar strategies to promote evidence-based practice implementation. Nursing leaders actively promote evidence-based practice implementation, work to influence evidence-based practice implementation processes and integrate evidence-based practice implementation into everyday policy and practices. CONCLUSION: The deliberative, conscious strategies nursing leaders used were consistent across country setting, context and clinical area. These strategies were based on a series of activities and interventions around promoting, influencing and integrating evidence-based practice implementation. We conjecture that these three key strategies may be linked to two overarching ways of demonstrating effective evidence-based practice implementation leadership. The two overarching modes are described as mediating and adapting modes, which reflect complex, dynamic, relationship-focused approaches nursing leaders take towards promoting evidence-based practice implementation. IMPACT: This study explored how nursing leaders promote evidence-based practice implementation. Acknowledging and respecting the complex work of nursing leaders in promoting evidence-based practice implementation through mediating and adapting modes of activity is necessary to improve patient outcomes and system effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".