Bibliographic record
Abstract
Introduction: Clinical education is one of the most important and crucial parts of Nursing education.Successful clinical education needs to adopt effective approaches that are based on up to date knowledge, clients' and patients' needs.Therefore, it can prepare the learners for learning.The aim of this study was to investigate the effect of model of Competency-Based Mastery learning on practical learning of nursing students.Material and methods: In a quasi-experimental study using pre-test and post-test with two group design, 28 nursing students were selected through census sampling method and placed randomly in 2 control and 2 experiment groups, each group containing 6 to 7 people.After taking the cognitive skills pre-tests, control group received traditional education and experiment group went under clinical education using Competency-Based Mastery Learning program for 12 days.As final step, the post-test cognitive skills were held and behaviors skill checklist were observed.Data was analyzed using SPSS software using Wilcoxon and Mann Whitney tests and Pearson correlation coefficient.Results: The findings showed that the participation of the Competency-Based Mastery Learning has been effective in promoting of their knowledge and skill.The mean scores of them significantly differed before and after the participating in Competency-Based Mastery Learning.and compare the clinical competency nurse training differed before and after in group of traditional education and Competency-Based Mastery Learning Group.Conclusion: Competency-Based Mastery Learning promises high level of learning for students, it seems Competency-Based Mastery Learning structured approve to maximizing opportunities for learning and professional development and clinical competency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.968 | 0.965 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".