Effect of Video vs. Lecture/Demonstration in Improving Nursing Interns’ Knowledge and Skills Regarding External Ventricular Drain (EVD): A Quasi-Experimental Study
Bibliographic record
Abstract
Background: Healthcare settings ought to consider creative strategies with regard to training nurses in clinical competencies, including trainee nurses and new nurses with limited resources. This study aimed to develop a high-quality educational video to gauge the effect of video instruction versus lecture demonstration in improving the skills and knowledge of nurse interns on the subject of external ventricular drain. Method: The study used a quasi-experimental post-test design and took place from June 2019 until May 2020. The 80 participants, all nurse interns, were randomly assigned to one of two teaching methods (video instruction or lecture demonstration). The data were gathered using a questionnaire prepared by the researchers. Results: The mean score of the lecture group was 68.2 +/-21.1, and in the video group, 78.5 +/-21.6. This means that the video group outperformed the lecture group in terms of skill (p=0.034). The findings showed no statistically significant difference in the groups’ overall knowledge and competence ratings. Conclusion: Video is a sound educational strategy, and the clinical education system can support the use of videos as a complementary method to teach clinical 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".