Integrated training (practicing, peer clinical training and OSCE assessment): a ladder to promote learning and training
Bibliographic record
Abstract
Introduction The teaching-learning process plays an effective role in training nursing students. Devising novel methods can lead to achievement of educational objectives as well as promotion of the clinical and practical training. The present study is aimed to determine the effect of integrated training, including practicing, peer clinical training and objective structured clinical examination (OSCE) assessment. Methods The interventional study was conducted on 58 freshman students. Two groups underwent a five-stage educational process; so that, all the students were trained and practiced in the skills lab, and their practical skills were investigated via the OSCE test. Afterwards, for clinical training in hospital, they were randomly divided into two groups of routine training (n = 26) and the peer-learning method (n = 32). Subsequently, in order to investigate the outcomes of the process, the OSCE test scores of the two training groups were compared both before and after the apprenticeship course. Results Scores of all the students were increased significantly at the end of the semester, but in terms of the total score of the clinical skills (14.79 ± 1.52 vs. 18.52 ± 0.84), the difference was insignificant (p = 0.29). Conclusion Training clinical skills along with OSCE practice and assessment can improve the nursing students' learning as well as their practical and clinical performance. Improvement of the students' performance can lead to high-quality care nursing.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".