Skills and Attributes Required by Clinical Nurse Specialists to Promote Evidence-Based Practice
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: The purposes of this article were to describe the challenges that clinical nurse specialists (CNS) face in their role and to examine how CNSs describe the skills and attributes that are needed to promote the use of evidence-based practice (EBP) in their workplaces. This article is based on findings from a dissertation regarding how CNSs promote EBP in a western Canadian province. DESIGN: A sequential explanatory participant selection mixed-methods design was used for this study. SETTING: The study took place in a western Canadian province that has a population of 1 million people, with 42.7% of the population residing in the 2 largest cities. SAMPLE: The sample was drawn from a provincial registered nurse database. The sample for the survey was 23, and for the interviews, there were 11 participants. METHODS: The telephone survey contained 113 questions grouped into several subcategories. SPSS 18 was used to analyze the survey data. The semistructured interviews were conducted face to face, transcribed, and reviewed for recurrent themes. Interpretive description was used to analyze the themes. FINDINGS: The major challenges faced by CNSs are role strain, lack of support and resources, and role ambiguity. The skills and attributes required to be a CNS are graduate preparation, clinical expertise, and people/communication skills. CONCLUSIONS: Clinical nurse specialists can improve patient outcomes by promoting EBP; to do so, they need to work in supportive contexts that give those in the CNS role a set of clear role expectations. IMPLICATIONS: There are challenges faced by CNSs in Canada, and there is a need to strengthen the CNS's role by standardizing the regulatory requirements at a national level.
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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 | 0.014 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.010 |
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; both teacher heads 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".