The effectiveness of latex wound model on wound dressing skill of the nursing students
Bibliographic record
Abstract
Wound dressing is a skill which must be able to be performed by all nurses. If the practical ability of a nurse is weak, it will affect the quality of patient care. The objective of this research is to study the effectiveness of the latex wound model for wound dressing training on wound dressing skills of the nursing student. The latex wound model is a device used in practice which is made from rubber. The samples are the 60 second-year nursing students. Simple random sampling was applied in the selection of samples to be an experimental group and a control group for 30 persons per group. The latex wound model was provided to the experimental group for wound dressing skill training at the dormitory for a seven day period. A personal data questionnaire, wet dressing skill evaluation form, and dry dressing skill evaluation form were used for data collection. The data were analyzed using descriptive statistics, Wilcoxon Signed Ranks Test and Mann-Whitney U Test. The results indicated that: 1) the mean score of wet dressing skill and dry dressing skill after the intervention were significantly higher than before the intervention (p < .05); 2) the mean score of wet dressing skill and dry dressing skill of the experimental group were higher than that of the control group, who was given the explanation of research procedures and the use of latex wound model, at statistical significance (p < .05). The findings imply that the use of the latex wound model for wound dressing training could enhance the wound dressing practical skill of the nursing students.
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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 | 0.003 |
| 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 | 0.000 |
| 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".