Clinical Instructors' Knowledge, Attitude, and Practice Regarding Evidence-Based Practice
Bibliographic record
Abstract
Background: The application of EBP has highly appreciated across healthcare disciplines to improve health care delivery. The utmost goal of nursing is the safe and compassionate patient care that can be achieved through utilization of best research evidence in making decisions for health care services. These expectations have laid the responsibility on clinical instructors to prepare their future nurses for this multifaceted role. Aim: Therefore, the present study aimed to determine the clinical instructors’ level of knowledge, attitude, and practice regarding EBP in the Pakistani context. Methodology: A cross-sectional study design was adopted. Clinical instructors (n=110) from two public and private sectors nursing institutes recruited in the study by using convenience sampling technique. A structured self-administered questionnaire was utilized. Data was entered in SPSS (Version 23.0) for analysis and descriptive analysis was done. Results: The participants were predominantly female clinical instructors (86.4%) as compared to male counterparts. Most of the study participants (62.7%) were having Bachelor of Science in nursing degree. Study yielded the good level of knowledge among most (96.4%) of the instructors whereas about half of the study participants (50.9%) showed moderate level of practice. Moreover, underdeveloped attitude was found among less than half (47.2%) of the participants. Conclusion: Generally, knowledge of clinical instructors regarding EBP was high, but attitude and practice were low among the clinical instructors.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".