Registered nurses' application of evidence‐based practice: a national survey
Bibliographic record
Abstract
BACKGROUND: Evidence-based practice (EBP) is a worldwide approach to improving health care. There is, however, a shortage of studies examining whether or not newly graduated health care professionals are actually applying EBP in their daily work. OBJECTIVES: To examine the application of EBP in clinical practice by registered nurses (RNs) 2 years post graduation and to explore whether the application of EBP differed with regard to the clinical settings where RNs were working. METHOD: A cross-sectional design using a national sample. Data were collected in 2007 from 987 RNs (response rate 76%). Six items measuring respondents' self-reported extent of applying EBP were used. Results Of the 987 RNs, 19% formulated questions and performed searches in data bases, 56% used other information sources, 31% appraised the literature, 30% participated in practice development and 34% participated in evaluating clinical practice. A greater proportion of the RNs working in elder care applied EBP compared with the RNs working in hospitals, psychiatric care and primary care. CONCLUSIONS: The RNs applied the components of EBP to a rather low extent 2 years post graduation despite EBP being an important objective in Swedish health care and educational programmes since the 1990s. These findings support other studies reporting the implementation of EBP in organizations as a complex and often slow process. The differences in the RNs extent of applying EBP in relation to their workplace indicate that contextual factors and the role of the RN in the organization are of importance for getting EBP into practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".