Forecasting Future Demand of Nursing Staff for the Oldest-Old in China by 2025 Based on Markov Model

Zhang, Liangwen; Shen, Shuyuan; Guo, Yaqian

doi:10.34172/ijhpm.2021.63

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…The scores were summed, and total scores ranged from 6 to 18. According to classification criteria in previous studies [ 52 , 53 ], the degree of disability of the spouse being cared for was classified as no disability (Note: The reason why some older adults being cared for in the sample are non-disabled is that CHARLS have some samples with unlimited ADLs but limited instrumental activities of daily living (IADLs), including doing household chores, cooking, shopping, taking medications, and money management.) (ADL = 6), mild disability (ADL = 7–10), moderate disability (ADL = 11–14), and severe disability (ADL = 15–18).…”

Section: Methodsmentioning

confidence: 99%

Spousal Care Intensity, Socioeconomic Status, and Depression among the Older Caregivers in China: A Study on 2011–2018 CHARLS Panel Data

Yang

et al. 2022

Healthcare

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Using the stress process model and data from the 2011–2018 China Health and Retirement Longitudinal Study (CHARLS), this study examined the effect of spousal caregiving intensity on the depression level of older caregivers in China. The moderating role that socioeconomic status plays in the relationship between spouses was explored by constructing multilevel growth models (MGMs). The care intensity for a spouse was found to relate to significantly increased depression levels in older caregivers, while the degree of disability of the spouse being cared for (B = 0.200, p < 0.001) having a greater effect on depression than the duration of care (B = 0.007, p < 0.01). There was a threshold effect where the provision of more than 10 h of care per week for a spouse (B = 0.931, p< 0.001; B = 0.970; p < 0.01) or caring for a disabled spouse with limited ADLs (B = 0.709, p < 0.01; B = 1.326; p < 0.001; B = 1.469, p < 0.01) increased depression in older caregivers. There were moderating influences, including higher professional prestige before retirement (B = −0.006, p < 0.05) and higher annual family income (B = −0.037, p < 0.10), that increased depression related to the spouse’s degree of disability. It was considered that active familism measures should be formulated for older spousal caregivers, especially those with lower socioeconomic status.

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Section: Methodsmentioning

confidence: 99%

Spousal Care Intensity, Socioeconomic Status, and Depression among the Older Caregivers in China: A Study on 2011–2018 CHARLS Panel Data

Yang

et al. 2022

Healthcare

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“…[16][17][18] According to the answers of participants on the six items, disability status was classified as the following four categories: non-disability was defined as being able to complete all the six items independently, 16,17 mild, moderate, and severe disability were defined as the presence of 1-2, 3-4, and 5-6 items requiring partial or complete help, respectively. 19 Given the results of previous studies on influencing factors of disability, 13,20 the predictive variables of this study included sociodemographic, lifestyle, physiological, mental and cognitive state, and family socioeconomic factors. The description of variables and their assignments are listed in Text S1 and Table S1.…”

Section: Measure Of Variablesmentioning

confidence: 99%

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study

Wu,

Xiang,

Wang

et al. 2024

Psychogeriatrics

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BackgroundOlder adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals.MethodsData were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008–2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008–2012, 2008–2014, and 2008–2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full‐variable set were used to predict 4‐, 6‐, and 10‐year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model.ResultsRandom forest in full‐variable set and XGBoost in LASSO set were the optimal models for 4‐year prediction. Support vector machine was the optimal model for 6‐year prediction on both sets. For 10‐year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini‐Mental State Examination, and psychological well‐being score were also important predictors.ConclusionsMachine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.

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“…Between 2014 and 2020, the number of officially registered elderly care workers in China increased from 194,100 to 378,900 [ 9 ]. However, the high turnover rate of China’s elderly care workers has led to a workforce gap of at least 5 million in the elderly care industry due to several reasons such as low wages [ 10 ]. Currently, each elderly care worker in China cares for an average of six elderly people every day, which is higher than the general standard of 4:1 in developed countries [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Perceived HRM and turnover intentions of elderly care workers: perspective from person-job fit and institutional ownership

Wu,

Chen,

Wan

et al. 2024

BMC Nurs

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Background Although the phenomenon of high turnover rate in the elderly care industry has existed for a long time, there are few studies that have constructed frameworks to comprehensively analyze the strength of the effects of various factors on the turnover intention of elderly care workers.. This study analyzed the impact of different types of perceived human resource management practices on elderly care workers’ turnover intentions and whether this relationship is moderated by person-job fit and ownership of elderly care institutions. Methods This is a cross-sectional and regional survey study. The study included questionnaire survey data from a total of 305 elderly care workers from 42 elderly care institutions in 21 provinces in China during June to September 2021. Descriptive statistics, Pearson correlation coefficient, multiple regression, and heterogeneity analyses were performed. Results Perceived work environment ($$\beta$$ β =-0.5164, p< 0.01), perceived occupational protection ($$\beta$$ β =-0.3390, p< 0.01), perceived welfare benefits ($$\beta$$ β = -0.2620, p< 0.01) and perceived competency training ($$\beta$$ β = -0.1421, p< 0.1) were all significantly and negatively related to turnover intentions, the quality of perceived work environment has the greatest impact on elderly care workers’ turnover intentions. Under the moderating effects of person-job fit and ownership of elderly care institutions, there existed heterogeneity between perceived human resource management and turnover intentions among elderly care workers. High level of person-job fit and elderly care institutions’ public feature can effectively weaken the negative impact of each type of perceived human resource management on turnover intention among elderly care workers. Conclusions The managers of elderly care institutions should optimize the management mechanism, typically pay attention to elderly care workers’ working environment, formulate and improve the professional standards and job requirements for elderly care workers, promote the public welfare value of nursing care services, and strengthen the sense of honor and responsibility of elderly care workers to reduce the turnover rate.

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Forecasting Future Demand of Nursing Staff for the Oldest-Old in China by 2025 Based on Markov Model

Cited by 8 publications

References 29 publications

Spousal Care Intensity, Socioeconomic Status, and Depression among the Older Caregivers in China: A Study on 2011–2018 CHARLS Panel Data

Spousal Care Intensity, Socioeconomic Status, and Depression among the Older Caregivers in China: A Study on 2011–2018 CHARLS Panel Data

Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study

Perceived HRM and turnover intentions of elderly care workers: perspective from person-job fit and institutional ownership

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