An attempt to improve nurses’ interest in and use of research in clinical practice by means of network support to ‘facilitator nurses’
Bibliographic record
Abstract
Background: Scientific knowledge is expected to be used in clinical practice to ensure that patients are given evidence-based nursing care. Therefore, in order to improve nurses’ research utilisation in clinical practice a network had been provided for nurses especially interested in nursing development in eleven wards. These nurses were expected to take on the role of key person (facilitator) for nursing development in clinical practice. Aim: The study was aimed at describing nurses’ interest in nursing research, how network support to ‘facilitator nurses’ could improve development in patient care based on evidence, and what hindering factors for such development could be. Methods: One and a half years after onset of the project a follow-up study was conducted with a questionnaire answered by 75 (64%) nurses, and group interviews with nine facilitators and eleven head nurses. Findings: The nurses’ interest in research utilisation was in general high and in eight wards development work had started. The facilitator nurses had mostly worked without involving their colleagues. Hindering factors for nursing development were related to time, EBP knowledge, involvement and the interest of head nurses and colleagues. Education, work place, previous participation in research projects, and participation in the network impacted positively on nurses’ attitudes to and interest in research. Conclusion and implication for clinical practice: Providing networks to ‘facilitator nurses’ in the ward could be useful for developing nursing care based on research findings. However, support from nurse leaders, involvement of the whole nursing staff, and training in research utilisation are important factors for success.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".