Evidence-Informed Education and Practice in Collaborative Learning Units: A Mixed-Methods Study
Bibliographic record
Abstract
Purpose: The primary purpose of this study was to evaluate evidence-informed practice knowledge, capacity, and skills of senior baccalaureate nursing students, nurses, and nursing faculty involved with the dedicated education unit/collaborative learning unit (DEU/CLU) practice education model. Secondary objectives were to 1) explore student, nurse, and faculty DEU/CLU practice education experience, and 2) obtain nurses views of the DEU/CLU practice education model and suggestions for improvement. Method: We used a convergent parallel mixed-methods design with a nested case study. Data were collected across 23 DEU/CLUs within three acute care facilities and one university with students (n = 18), nurses (n = 97), and academic faculty (n = 7) for 122 participants. Survey questions used 5-point Likert scales and were analyzed using proportion of positive responses, stratified by group. Differences between groups were assessed using confidence intervals. Four focus groups for nurses (n = 10) were conducted across six clinical units in two facilities to enhance richness of nurses’ views of the DEU/CLU practice education model. NVivo software was used to support inductive thematic analysis of qualitative data. Results: Students, nurses, and academic faculty had similar patterns of evidence-informed practice knowledge, capacity, and skills, except for integration of research findings into quality improvement initiatives, where faculty and nurses reported significantly more knowledge than students. Nurses and academic faculty expressed concerns regarding limited opportunities for collaboration between practice and education, challenges with communication around student progress, and insufficient resources to support the DEU/CLU learning model. Nurses reported benefits of DEU/CLUs including collaborative team learning for students and opportunity for independence, while challenges included students with greater learning needs “falling through the cracks” in the absence of a consistent nurse overseeing their learning progress. Nurses suggested a DEU/CLU and preceptor hybrid approach, titrated for transitioning between the two based on the individual students’ capacity, confidence, and comfort levels. Nurses expressed that the DEU/CLU model enables evidence-informed nursing practice through practice–academic collaboration provided that experienced nurses are available, and unit census and nurse–patient ratios are appropriate. Conclusion: Critical resources for student clinical education and evidence-informed clinical learning environments include strong inter-institutional collaborative partnerships, nurse and academic faculty mentoring and orientation, and formalized communication channels. Adequately supported DEU/CLUs with available experienced nurses and appropriate nurse–patient ratios, in collaboration with strong practice–academic partnerships enable development of evidence-informed nursing skills and 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 | 0.071 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".