Education and mental health: Do psychosocial resources matter?

Abstract

Prevalence rates for mental health problems are higher when an individual's socioeconomic status (SES) is low, but the underlying mechanisms are not clearly understood. We investigated associations between education as indicator for SES and depressive symptoms as well as positive mental health (PMH). Moreover, we hypothesized that low education is associated with a lack of psychosocial resources and more daily hassles, which in turn mediate the relationships between education and mental health. In a German representative sample ( N = 7937), we cross-sectionally first examined whether a person's educational level was associated with depressive symptoms and PMH. Educational level was defined as the highest academic qualification achieved. Second, we investigated whether also sense of control, resilience, delay of gratification, cultural activity and daily hassles followed gradients along the educational level. Third, we investigated whether they mediated the relationship between education and mental health. Results showed that depressive symptoms measured by items from the DASS-42 depression subscale were more prevalent for persons with a low educational level, PMH operationalized by the Positive Mental Health Scale was equally distributed, and all psychosocial characteristics followed the gradient of educational level. In addition, the group with a high school diploma was particularly burdened. Structural equation modeling indicated that the associations between education and mental health were mediated by all psychosocial characteristics and daily hassles, apart from the delay of gratification. In the group with the lowest educational level the model fit indices for depressive symptoms and PMH were acceptable ( χ2 = 10007.243 (627), CFI = 0.869, RMSEA = 0.04 (90% CI [0.04, 0.04], SRMR = 0.05; and χ2 = 12779.968 (741), CFI = 0.86, RMSEA = 0.05 (90% CI [0.05, 0.05], SRMR = 0.05), respectively). The effect size P m refers to the proportion of the total effect that is mediated by one or more variables (“M”), and the effect size of all indirect effects in the model for depressive symptoms was P m = .80 and for PMH it was P m = .68. The results support our hypotheses that low education is associated with less psychosocial resources, which in turn serve together with daily hassles as pathways between education and depressive symptoms as well as PMH. Building on these findings, longitudinal studies are necessary to investigate causality.

Psychological Functioning of Slovene Adults during the COVID-19 Pandemic: Does Resilience Matter?

“…This finding is also consistent with previous research showing somewhat higher susceptibility of women to elevated levels of stress and mental health problems than men [25]. Finally, in line with previous findings [26,27], our results indicated a protective role of higher education in good psychological functioning, although this association was weak and diminished to the level of insignificance after controlling for personal resilience.…”

What We Have Learned from Two Decades of Epidemics and Pandemics: A Systematic Review and Meta-Analysis of the Psychological Burden of Frontline Healthcare Workers

“…It is possible that the participants in the comparison group differed from the specialized and highly educated medical staff with regard to educational and socioeconomic background. The fact that lower educational level and socioeconomic status are associated with worse physical and mental health [ 136 , 137 ] might be another explanation for why these participants fared worse psychologically than those on the frontlines. Moreover, it is important to bear in mind that the general public may have faced a greater risk of becoming unemployed during the pandemic than the hospital staff [ 99 ], thus possibly adding to the psychological distress.…”

The Association between Urbanization and Depression among the Middle-Aged and Elderly: A Longitudinal Study in China

“…Depression often occurs in people who are older, female, low-educated, non-spousal, less social support and relatively unhealthy. 32 - 34 Therefore, we use age, square of age, gender, education level, marital status and work status as covariates. Among these variables, gender is unchanged due to the use of longitudinal data, education level is found unchanged through descriptive statistics, and the rest were time-varying variables ( Table 1 ).…”