Psychometric properties of the Chinese version of Spiritual Index of Well-Being in elderly Taiwanese
Bibliographic record
Abstract
BACKGROUND: Spiritual well-being has become an increasingly important issue for the elderly people. The 12-item Spirituality Index of Well-Being (SIWB) is a well-validated instrument for assessing a patient's current spiritual state. However, the psychometric properties of the SIWB in the Chinese elderly populations are not known. Therefore, this study translated the SIWB into Chinese and evaluated its psychometric properties. METHODS: The English version of the SIWB was first translated into Chinese based on the Brislin's translation model. The psychometric properties of the translated version of the SIWB (SIWB-C) was evaluated in 416 elderly Taiwanese recruited using a purposive sampling procedure from a medical center, a long-term care institution, and a community health center. Convergent validity was accessed using Pearson's correlation coefficients of the SIWB-C, the EQ-5D-3 L health-related quality of life scale, and the Geriatric Depression Scale-5 (GDS-5). Exploratory factor analysis with Varimax rotation was performed to determine the construct validity. Confirmatory factor analysis was conducted for verification of the quality of the factor structures and demonstrating the convergent validity of the SIWB-C. An internal consistency test based on the Cronbach's alpha coefficient and a stability test based on the Guttman split-half coefficient were also performed. Test-retest reliability was evaluated with intraclass correlation coefficient. RESULTS: Exploratory factor analysis confirmed the original two-dimensional structure of the scale. Confirmatory factor analysis indicated a well-fitting model and a fine convergent validity of the SIWB-C. The Cronbach's alpha coefficient and the Guttman split-half coefficient for the SIWB-C were 0.94 and 0.84, respectively. The correlations between the SIWB-C with EQ-5D-3 L and GDS-5 were 0.22 (p < 0.01) and 0.45 (p < 0.05), respectively. The intraclass correlation coefficient of the SIWB-C over a test-retest interval of two weeks was 0.989. CONCLUSIONS: The SIWB-C was found to be a potential useful measure of subjective spiritual well-being in elderly Taiwanese. Its application in assessing the spiritual well-being in Mandarin-speaking elderly population warrants further investigation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".