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Sun X, Gao Y, Chen Y, Qin L, Lin Y, Song J, Zhang Z, Wang H, Feng H, Tan H, Chen Q, Peng L, Dai W, Wu IXY. Development and validation of frailty and malnutrition knowledge assessment scale for community-dwelling older adults. Appl Physiol Nutr Metab 2023; 48:974-1004. [PMID: 37669568 DOI: 10.1139/apnm-2023-0141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
There is a lack of reliable tools to assess the knowledge of frailty and malnutrition in community-dwelling older adults. To develop and validate reliable frailty and malnutrition knowledge assessment scales for this population, two scales were developed and validated through five phases. Phase 1: the item pools were constructed through a literature review and research panel based on the symptom interpretation model. Phase 2: the expert consultation was performed to select the items. Phase 3: a pilot survey was conducted to assess the clarity of the items and further revise the scales. Phase 4: 242 older adults were surveyed to finalize the items. Phase 5: 241 older adults were surveyed to test the psychometric properties. The two scales each comprise 3 dimensions (symptoms, risk factors, and management strategies) and 11 items. They had good construct validity, with all indicators of correlation analysis and confirmatory factor analysis meeting their specific criteria. The reliability of the frailty and malnutrition knowledge assessment scales was good, with composite reliability coefficients all >0.60, Cronbach's alpha being 0.81 and 0.83, and the Spearman-Brown coefficient being 0.74 and 0.80, respectively. Their acceptability was good, with both having a completion rate of 92.18% and an average completion time of 3 min. The two scales are reliable tools to assess the knowledge of frailty and malnutrition among community-dwelling older adults, especially for large-scale surveys. They can help identify knowledge gaps in older adults and provide a basis for developing targeted educational interventions.
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Affiliation(s)
- Xuemei Sun
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
| | - Yinyan Gao
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
| | - Yancong Chen
- Changsha Municipal Center for Disease Control and Prevention, Changsha, China
| | - Lang Qin
- Sinocare Inc., No. 265 Guyuan Road Hi-tech Zone, Changsha, Hunan, China
| | - Yali Lin
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
| | - Jinlu Song
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
| | - Zixuan Zhang
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
| | - Huan Wang
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
| | - Hui Feng
- Xiangya Nursing School, Central South University, Changsha, China
| | - Hongzhuan Tan
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
| | - Qiong Chen
- Xiangya Hospital of Central South University, Changsha, China
| | - Linlin Peng
- Xiangya Hospital of Central South University, Changsha, China
| | - Wenjie Dai
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
| | - Irene X Y Wu
- Xiangya School of Public health, Central South University, Changsha, Hunan, China
- Hunan Provincial Key Laboratory of Clinical Epidemiology, Changsha, China
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Zhou Y, Chen XY, Liu D, Pan YL, Hou YF, Gao TT, Peng F, Wang XC, Zhang XY. Predicting first session working alliances using deep learning algorithms: A proof-of-concept study for personalized psychotherapy. Psychother Res 2022; 32:1100-1109. [PMID: 35635836 DOI: 10.1080/10503307.2022.2078680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
- Ying Zhou
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiao-yu Chen
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Ding Liu
- College of Psychology, Shenzhen University, Shenzhen, People’s Republic of China
| | - Yu-lin Pan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China
| | - Yan-fei Hou
- Department of Humanities and Mental Nursing, School of Nursing, Southern Medical University, Guangzhou, People’s Republic of China
| | - Ting-ting Gao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
| | - Fei Peng
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
- Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiao-cong Wang
- Department of Obstetrics and Gynaecology, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Xiao-yuan Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China
- Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
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