Effect of peer evaluation training on senior nursing students’ performance enrolled in nursing administration course
Bibliographic record
Abstract
Objective : Senior nursing students have to be active participants in their learning process; this can be done through peer evaluation, hence they need to be trained to provide and accept constructive feedback to help their professional growth. So, this study aimed to assess the effect of peer evaluation training on senior nursing students’ performance in nursing administration course. Methods : The subject included all (152) available senior nursing students enrolled in nursing administration course at faculty of Nursing-Tanta University. Peer evaluation knowledge test (25 questions), nursing students’ peer evaluation attitude scale (31 items) and nursing student’s peer evaluation checklist (65 items) were used to collect the study data. Results : Experimental nursing students group's total knowledge and performance about peer evaluation were significantly improved post than pre training sessions and than comparison nursing students group. Majority of experimental nursing students group agreed that peer evaluation was beneficial. Significant positive relation at P ≤ .05 was found between the experimental and comparison nursing students groups’ total level of knowledge, their attitude and peer evaluation performance post-sessions. Conclusions : Senior nursing students’ knowledge, performance and attitude about peer evaluation were improved after implementation of the training sessions. So, peer-evaluation method is recommended to be integrating into formal learning activities and establishing trustful reassuring learning environment.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".