Automated Author ProfileAem-Orn Saengsiri
Aem-Orn Saengsiri
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The purpose of this survey research for causal analysis was to examine the relationships between cardiac self-efficacy, social support, left ventricular ejection fraction, angina, dyspnea, depression, vital exhaustion, functional performance, and quality of life in coronary artery disease patients (CAD) post Percutaneous Coronary Intervention (PCI). The conceptual framework was guided by the revised Wilson and Cleary model. 303 patients with coronary artery disease post PCI participated in this study. The research instruments included demographic data questionnaire, quality of life index-cardiac version IV, Cardiac Self-efficacy Scale, the Social Support Questionnaire, the Rose questionnaire for angina, the Rose Dyspnea Scale, the Center for Epidemiologic Studies Depression Scale, the short-form health survey: vitality subscale (VT), and Functional Performance Inventory Short-Form, having reliability ranging from 0.72 to 0.98. Data were analyzed using descriptive statistic and a linear structural relationship (LISREL) analysis.The results showed that the hypothesized model fit the empirical data and explained 54% of the variance in quality of life (chi-square = 1.90, df=3, p=.59, chi-square/df=.63, RMSEA=.00, GFI=.99, AGFI=.98). The significant factors directly affected on quality of life of CAD patients post PCI were social support, depression, vital exhaustion and self-efficacy, the value of standardized path coefficients were .307, .239, .235, and .205, respectively. Self-efficacy is the only variable that had indirect effect on quality of life (β = .212, p
Authors
- Aem-Orn Saengsiri