Improvement of nursing care by means of the evidence based practice process: The facilitator role
Bibliographic record
Abstract
Background: This study is as part of a comprehensive project aimed at implementing and evaluating a model (Collaborative Model of Best Practice, CMBP) for promoting evidence-based practice (EBP) in health care contexts. Nurses and nurse teachers were engaged as facilitators. Aim: In this paper the aim was to explore facilitators’ experiences of their role in the EBP-process in medical/surgical wards at two Swedish hospitals. Methods: Five focus group interviews were conducted with two groups of facilitators, four nurses and one nurse teacher in each group, all together ten interviews. Data was analyzed according to the method of inductive content analysis. Findings: The facilitator role was described as comprehensive, dynamic, and changing, which put heavy demands on the facilitators. Being in the role meant shouldering a leadership role filled with many responsibilities, together with one’s own professional and personal development. Ongoing, timely and adequate support was essential in order to succeed with implementation of evidence-based new routines. Conclusions: The study shows that the CMBP model with nurse- and teacher facilitators working together could impact positively on the implementation of new routines on hospital wards. Our findings resonate with other studies showing that change in practice is a challenging and strenuous activity that needs long preparation in advance for all parties involved, and most of all puts demand on comprehensive support. Long-lasting activities are needed to make sure that prerequisites given in an EBP project like this one really are working in the decided direction.
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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 | 0.051 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".