The Effect of Applying Direct Observation of Procedural Skills (DOPS) on Nursing Students' Clinical Skills: A Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Remarkable advances in educational measurement have proved need to the implementation of modern and appropriate methods of clinical evaluation. This study was carried out to compare the effect of applying direct observation procedural skills and routine evaluation method on clinical skills of nursing students. METHODS: This randomized clinical trial was conducted on students of Nursing Army College, Tehran, Iran. After obtaining approval from the Ethics Committee of the Baqiyatallah University of Medical Sciences Research Deputy, all nursing students and instructors who agreed to participate in this study sign the informed consent. The participants were randomly assigned into intervention and control groups. After the teachers were trained and an inter-raters reliability test was conducted, evaluation was performed through DOPS in the intervention group while the control groups were evaluated through the routine method. Assessment checklists for two procedures (Intra venous catheterization and change dressing) were valid and reliable. Finally data were analyzed through descriptive and analytical statistics (Chi-square, t-test, Repeated Measure ANOVA) using SPSS version 16. RESULTS: No significant difference was observed between the two groups in terms of demographic variables (P>0.05), but a significant difference was observed between intervention and control scores (P=0.000). In other words, application of DOPS has improved clinical skills of the students significantly. CONCLUSION: Using this new method improved the students' scores in clinical procedures implementation; therefore, we suggest that nursing colleges apply this evaluation method for clinical education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".