Examining Clinical Instructors' Understanding, Perspectives, and Implementation of Evidence-Based Practice (EBP): A Multi-Institutional Study
Bibliographic record
Abstract
The adoption of evidence-based practice in healthcare has gathered significant appreciation across disciplines, aiming to improve patient care outcomes. In nursing, the primary objective is to deliver safe and standardized care by integrating evidence-based practice into clinical decision-making. Consequently, nursing educators must equip future nurses with the skills to decrease mortality rates and enhance patients' quality of life through the utilization of the best available evidence. Objectives: To assess the understanding, perspectives, and implementation of evidence-based practice among clinical instructors across five nursing educational institutes. Methods: A descriptive cross-sectional design was employed for this study. A total of 110 clinical instructors from both public and private sector educational institutes were recruited using convenience sampling. Participants completed a structured self-administered questionnaire, and data were measured using descriptive and inferential statistics in SPSS (Version 23.0). Results: Clinical instructors possessing master's degrees demonstrated a good understanding of evidence-based practice steps and their application. On the other hand, no significant differences (p-value>0.05) were received in overall perspectives towards evidence-based practice based on gender, qualification, and experience. Conclusions: It was concluded that clinical instructors with master's degrees demonstrated favourable perspectives and practices towards evidence-based practice. Female showed higher knowledge scores, while male excelled in perspectives and implementation.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".