Chinese Translation and Psychometric Testing of the Peripheral Intravenous Catheter Insertion Self-Confidence Scale
Bibliographic record
Abstract
Background and Purpose: There is no tool in China to evaluate self-confidence in peripheral intravenous catheter insertion. This study aims to translate the Peripheral Intravenous Catheter Insertion Self-Confidence Scale (PVCS) into Chinese and evaluate its reliability and validity among nursing students. Methods: The translation and validation followed Brislin's model, including forward translation, back-translation, synthesis, expert review and pre-testing to develop the Chinese version. A convenience sample of 205 nursing students from Tianjin and Chongqing, China, was surveyed to assess reliability and validity. Results: The Chinese version of the PVCS (PVCS-C) comprises 15 items, in two dimensions: theoretical knowledge self-confidence (5 items) and skill operation self-confidence (10 items). The scale's Cronbach's α coefficient was 0.979, the split-half reliability was 0.944, and the test-retest reliability was 0.985.The average scale-level content validity index (S-CVI/Ave) was 0.966, and the item-level content validity index (I-CVI) ranged from 0.875 to 1.000. The exploratory factor analysis (EFA) extracted two common factors, with a cumulative variance explained of 85.839%. For confirmatory factor analysis, the structural equation model fitting indices showed that the root mean square error of approximation (RMSEA) was 0.077, and the chi-square/degree of freedom ratio (χ2/df) was 2.22. Conclusion: The PVCS demonstrates excellent reliability and validity, making it a suitable tool for assessing nursing students' self-confidence in peripheral intravenous catheter insertion.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".