Clinical Nurse Specialists' Approaches in Selecting and Using Evidence to Improve Practice
Bibliographic record
Abstract
UNLABELLED: ABSTRACT Background: Evidence-based practice (EBP) has become the desired standard within all health disciplines because the integration of the best evidence into clinical practice is fundamental to optimizing patient outcomes. The valuing of research and research-based knowledge as the basis for decision making is explicit in current discourse in the health sciences. Despite the desires of proponents of EBP for use of evidence derived through research, nurses prefer to use knowledge derived from experience and social interactions. The clinical nurse specialist (CNS) is in the ideal position to act as a link between evidence and practice; however, a paucity of knowledge exists on how CNSs select and use evidence in their daily practice. PURPOSE: The purpose of this descriptive, cross-sectional study was to examine the approaches used by CNSs to select and use evidence in their daily practice. METHOD: A telephone survey, developed for this study from a pilot study conducted by the principal investigator (PI), was used to elicit responses from a purposive sample of CNSs living in a western Canadian province who were willing to be contacted for research, and who had practiced clinically as CNSs within the past year. A response rate of 75% (n = 94) was achieved. Descriptive statistics were used to describe and compare the variables of interest. RESULTS: Literature tailored to particular specialties and personal experiences were reported as the most frequently accessed sources of evidence. This evidence was most often used to facilitate improvements in patient care, and least often used to develop further research proposals. CONCLUSION: This study indicates that although CNSs select and use evidence from a wide variety of sources, further development of their capacity to retrieve and transfer knowledge may increase the uptake of research findings in 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 | 0.030 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".