Undergraduate nursing students’ perceptions of integrating evidence into practice in a psychiatric-mental health nursing clinical course
Bibliographic record
Abstract
Faculty in a baccalaureate nursing program recognized the need to make intentional efforts to assist nursing students in integrating evidence-based knowledge in their clinical learning. This paper aims to describe ways in which evidence-based practice (EBP) was incorporated into Psychiatric-Mental Health Clinical Course (P-MHCC) assignments, and summarize students’ perceptions about their use of evidence in their clinical practice. Content analysis was performed on 64 student essays. Four themes were identified following analyses of students’ written self-reflections: clinical assignments created opportunities to incorporate EBP, assignments promoted exploration of resources and fostered development of a professional self-concept, use of evidence in mental health nursing, and students’ views on future use of EBP and barriers. Clinical assignments and educational experiences were influential in students’ utilization of EBP. Students agreed on the value of EBP in their development as nurses and as being necessary to provide the best care to patients. Three future recommendations were proposed. First, faculty facilitating clinical courses must integrate EBP as a consistent component across all clinical assignments and employ specific rubrics to help students recognize the importance of evidence in clinical practice. Second, faculty must be proactive to explore and address students’ barriers to integration of EBP in a P-MHCC. Finally, clinical faculty should consider the use of EBP in clinical assignments as one of the strategies to promote students’ professional self-concept. Future research should include systematically exploring and testing the teaching strategies used by nursing faculty at all levels of the baccalaureate program to determine their effectiveness in improving baccalaureate-nursing students’ knowledge and skills.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".