KSPM
Search
- Page Path
- HOME > Search
English Abstract
- Measuring Workload of Home Visit Care Activities Using Relative Values.
- Seong Ok Han, Eun Cheol Park, Dae Ryong Kang, Im Ok Kang
- J Prev Med Public Health. 2008;41(5):331-338.
- DOI: https://doi.org/10.3961/jpmph.2008.41.5.331
- 4,814 View
- 56 Download
-
Abstract
PDF - OBJECTIVES
The purpose of this study was to measure the workload of home visit care activities and their relative values. This study examined also factors that affect the workload of home visit care activities. METHODS: The participants of this study were 126 homehelpers of 50 home visit care agencies at the 2nd Longterm Care Insurance Demonstration Project. The workload of home visit care activities was divided into total work and four dimensions ; physical efforts, mental efforts, stress and time. Home visit care activities consisted of four categories with 24 items. We used magnitude estimation method to measure their relative values of the four dimensions. The participants answered the relative values of each activities based on the reference service. We used the activity for supporting their elderly's evacuation as the reference service. RESULTS: Most of the respondents were over 40 years old female. They consumed most their time supporting elderly's going out. They consumed their highest physical, mental efforts, and stress for activities of coping with emergency situation. The Pearson correlation coefficients showed significant relationships between workload and each dimensions. This study showed that all four dimensions are statistically significant predictors of workload of home visit care activities. Also, we found that the home-helper's career affects the workload of home visit care activities. CONCLUSIONS: The workload of home visit care activities could be explained by physical efforts, mental efforts, stress and time. -
SummaryOriginal Article
- Statistical Methods for Multivariate Missing Data in Health Survey Research.
- Dong Kee Kim, Eun Cheol Park, Myong Sei Sohn, Han Joong Kim, Hyung Uk Park, Chae Hyung Ahn, Jong Gun Lim, Ki Jun Song
- Korean J Prev Med. 1998;31(4):875-884.
- 2,581 View
- 22 Download
-
Abstract
PDF
- Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the Em algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which used only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.
-
Summary
First
Prev
