Background Evidence about the impact of the COVID-19 pandemic on existing health inequalities is emerging. This study explored differences in mental health, sense of coherence (SOC), sense of community coherence (SOCC), sense of national coherence (SONC), and social support between low and high socioeconomic (SES) groups, and the predictive value of these predictors for mental health. participants and procedure A cross-sectional study was conducted using an online survey in the Netherlands in October 2021, comprising a total of 91 respondents (n = 41, low SES; n = 50, high SES). results There were no differences in mental health, SOC, SOCC, SONC, and social support between the groups. SOC was a predictor for mental health in both groups and SOCC for the low SES group. conclusions We found that both SOC and SOCC predict mental health during the pandemic. In the article we reflect on possible pathways for strengthening these resources for mental health.
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The COVID-19 crisis impacts populations globally. This impact seems to differ for groups with low- and high-socioeconomic status (SES). We conducted a qualitative study in the Netherlands using a salutogenic perspective to examine experiences with stressors and coping resources during the pandemic among both SES groups to gain insight on how to promote the health and well-being of these groups. We conducted 10 focus group discussions and 20 interviews to explore the experiences, including resources and stressors, of respondents from low- (N = 37) and high-SES (N = 38) groups (25-55 years, Dutch speaking). We analyzed the findings at individual, community, and national levels. The results show that coping depends on government-imposed measures and the way individuals handle these measures; restriction to the home context with positive and negative consequences for work and leisure; psychological negative consequences and resourcefulness; and social effects related to unity (e.g. social cohesion or support) and division (including polarization). Respondents with lower SES expressed more problems with COVID-19 measures and experienced more social impact in their neighborhood than those with higher SES. Where low-SES groups especially mentioned the effects of staying at home on family life, high-SES groups mentioned effects on work life. At last, psychological consequences seem to differ somewhat across SES groups. Recommendations include consistent government-imposed measures and government communication, support for home schooling children, and strengthening the social fabric of neighborhoods.
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Specific approaches are needed to reach and support people with a lower socioeconomic position (SEP) to achieve healthier eating behaviours. There is a growing body of evidence suggesting that digital health tools exhibit potential to address these needs because of its specific features that enable application of various behaviour change techniques (BCTs). The aim of this scoping review is to identify the BCTs that are used in diet-related digital interventions targeted at people with a low SEP, and which of these BCTs coincide with improved eating behaviour. The systematic search was performed in 3 databases, using terms related to e/m-health, diet quality and socioeconomic position. A total of 17 full text papers were included. The average number of BCTs per intervention was 6.9 (ranged 3–15). BCTs from the cluster ‘Goals and planning’ were applied most often (25x), followed by the clusters ‘Shaping knowledge’ (18x) and ‘Natural consequences’ (18x). Other frequently applied BCT clusters were ‘Feedback and monitoring’ (15x) and ‘Comparison of behaviour’ (13x). Whereas some BCTs were frequently applied, such as goal setting, others were rarely used, such as social support. Most studies (n = 13) observed a positive effect of the intervention on eating behaviour (e.g. having breakfast) in the low SEP group, but this was not clearly associated with the number or type of applied BCTs. In conclusion, more intervention studies focused on people with a low SEP are needed to draw firm conclusions as to which BCTs are effective in improving their diet quality. Also, further research should investigate combinations of BCTs, the intervention design and context, and the use of multicomponent approaches. We encourage intervention developers and researchers to describe interventions more thoroughly, following the systematics of a behaviour change taxonomy, and to select BCTs knowingly.
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