Does social participation make Thai psychologically abused elders happier? a stress-buffering effect hypothesis

ABSTRACT

This study investigates the influence of psychological elder abuse on life satisfaction levels in Thailand. This study also analyses the stress-buffering effect of social participation on the life satisfaction levels of Thai mentally abused elderly. Elder abuse has been proven to dramatically reduce Thai elders’ levels of life satisfaction as their function in society shrinks owing to ageism. As a result, individuals are more likely to lose their independence and status and be forced to rely on others, increasing the danger of abuse. Elder abuse has a more significant negative impact on life satisfaction levels among Thai older women. Nonetheless, the mentally abused elderly who participate in social activities are happier than those who do not. Thai elders who live with their daughters are more satisfied in life than those who do not, but living with adult offspring does not assist psychologically abused elders in escaping their psychological suffering.

KEYWORDS:

• Elder abuse
• life satisfaction
• older adults
• stress-buffering

Acknowledgments

The authors would like to thank the College of Population Studies (CPS), Chulalongkorn University, for allowing them to use the datasets collected by the College of Population Studies, Chulalongkorn University, in 2016 as part of the “Population Change and Well-being in the Context of an Aging Society Survey: Mega Project” research.

The author would like to express their gratitude to the editor(s) and reviewers for their considerate, constructive, and insightful comments on this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Raw data were generated at the College of Population Studies, Chulalongkorn University. Derived data supporting the findings of this study are available from the corresponding author, Y.A. on request.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. Life satisfaction and happiness are used interchangeably in the literature on happiness economics (Easterlin et al., 2011; Lim et al., 2019). Previous research has indicated that either “happiness” or “life satisfaction” delivers similar results (DiTella & MacCulloch, 2008).

2. According to this study, life satisfaction levels must be divided into three categories: i) low (scores 0–3), ii) moderate (scores 4–6), and iii) high (scores 7–10) in order to address the negatively predicted probability. However, information loss can be a major concern when life satisfaction levels are grouped into three categories.

3. For categorical variables, however, standardized effects are typically disregarded. For example, it is unreasonable to consider one standard deviation (SD) increase in a dummy variable such as gender (Williams, 2020). In this survey, the vast majority of variables are categorical. It is, therefore, plausible to interpret the effect of each categorical predictor on life satisfaction using unstandardized coefficients and odds ratios. The conventional justification for using standardized coefficients is that they permit a comparison of the effects of measured variables for different metrics (Williams, 2020).

4. According to Thailand’s National Education Plans in 1936 and 1951, the compulsory education level for Thai people was Prathom 4 (the 4 year primary education) (Sangnapaboworn, 2007, p. 264). Hence, older people of this study were still under Thailand’s education plans in 1936 and 1951.

5. Thai older persons receive a monthly old-age allowance of between 600 to 1,000 baht (The Parliamentary Budget Office, 2018).

6. A low R-squared in regression equations is expected in the social sciences, particularly for cross-sectional analysis. Notably, a low R-squared does not inherently indicate that an OLS regression equation is useless. The estimated models may accurately assess the relationship between independent and dependent variables (Ozili, 2023; Wooldridge, 2013).

7. The central limit theorem (CLT) $\left[n\to \mathrm{\infty },\sqrt{n}\left(\bar{x}-\mu \right)\stackrel{d}{\to }N\left(0,{\sigma }^2\right)\text{break}or\frac{\sqrt{n}\left(\bar{x}-\mu \right)}{\sigma }\stackrel{d}{\to }N\left(0,1\right)\right]$ states that, as n approaches infinity, the random variable $\left[\sqrt{n}\left(\bar{x}-\mu \right)\right]$converges to a random variable that follows a normal distribution with a mean of 0 and a variance of ${\sigma }^2$ or, equivalently, the random variable $\left[\frac{\sqrt{n}\left(\bar{x}-\mu \right)}{\sigma }\right]$converges to a random variable that follows the standard normal distribution (X. Zhang et al., 2022). The regression coefficients are asymptotically normally distributed with large sample sizes (n ≥500) when the CLT is applied to empirical investigations (Knief & Forstmeier, 2021). Consequently, linear regression models with residuals diverging from the normal distribution frequently produce accurate findings, particularly with large sample sizes (Schmidt & Finan, 2018).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.