Strategies to Increase Research-based Practice
Bibliographic record
Abstract
PURPOSE: A major focus of clinical nurse specialist nursing practice is the integration of research findings into practice. The purpose of this study was to describe strategies used to facilitate research utilization (RU) by nurses in a practice setting. DESIGN: This multiple-case study identified the strategies that clinical nurse specialists and master's degree-prepared nurse educators, working collaboratively, used to facilitate RU. SETTING/SAMPLE: The setting included 8 units in 4 sites of a university hospital with all willing nurses participating. METHODS: Open-ended focus groups and individual interviews and observational sessions were conducted using investigator-designed interview guides. Comprehensive qualitative analysis led to identification of categories and themes related to RU and the unit culture that supported it. FINDINGS: Findings demonstrated that strategies to facilitate RU by staff at the unit level included conducting original research, supporting nurses participating in research, assessing and meeting staff learning needs, promoting staff attendance at conferences, stimulating goal-setting for presentations and publications, encouraging and responding to new ideas, questioning practice and stimulating inquiry, capitalizing on expertise in research knowledge and skills, and generating information and material resources. Characteristics of unit culture were linked to varying degrees of success with these strategies. The interplay of strategies with unit culture and research-based practice is described. CONCLUSION: A wide repertoire of strategies is needed to facilitate RU, and the outcome of these strategies is influenced by the unit culture. IMPLICATIONS FOR PRACTICE: Consideration of the findings and the scope of the strategies used by nurses in the study can help clinical nurse specialist and other nursing leaders facilitate the building of practice on research.
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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 | 0.280 | 0.308 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.005 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.010 | 0.046 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".