Translation and validation of the traditional Chinese NLN educational practices questionnaire, simulation design scale and student satisfaction and self-confidence in learning
Bibliographic record
Abstract
Background and objective: Globally, the use of clinical simulation has been incorporated in different nursing programs. It is important to evaluate simulation using reliable and valid instruments. Using the same instrument helps to evaluate simulation under the same criteria both nationally and internationally. The National League of Nursing developed three simulation scales which is widely used in different countries and demonstrates a good reliability and validity. Nevertheless, it is only available in English. The aim of the study was to translate the original NLN simulation evaluation scales into Traditional Chinese and evaluate its psychometric properties.Methods: Beaton and colleague’s (2000) cross-cultural adaptation guidelines was adopted. Cronbach’s alpha coefficient (α) and Corrected item-total correlation was used to determine the internal reliability. Haccoun’s single group technique was used to assess the equivalent of the scale in the original and the translated version. Lastly, Exploratory Factor Analysis (EFA) was used to determine the factor structure and Intra-Class Correlation Coefficient (ICC) to test the stability of translated scale.Results: Nine simulation experts from Hong Kong, Mainland China, Singapore and Taiwan confirmed translation of the NLN scales (EPQ-C, SDS-C, SSCL-C). Cronbach’s alpha of all subscales and overall scales were acceptable (0.72-0.89). The intra-language, inter-language and temporal inter-language cross correlations between the original and translated scales were correlated (p < 0.01). ICC of the translated scales ranges from good to excellent (0.78-0.91). Lastly, EFA also demonstrated the items were theoretically coherent (≥ 0.40) and have the same factor structure as the original English version.Conclusions: Traditional Chinese NLN simulation evaluation scales demonstrated strong validity and reliability.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".