scholarly journals The architecture of psychological well-being: A network analysis study of the Ryff psychological well-being scale 

2021 ◽  
Author(s):  
Ana Blasco-Belled ◽  
Carles Alsinet
Keyword(s):  
Mental Health ◽  
Network Model ◽  
Network Models ◽  
Well Being ◽  
Mental Health Research ◽  
Psychological Well Being ◽  
Four Dimensions ◽  
Psychological Phenomena ◽  
Self Acceptance ◽  
The Stability

Abstract The proliferation of mental health research is orienting its efforts towards the exploration of psychological well-being. One of the main burdens is the measurement challenges reported by the Psychological Well-being Scale (PWBS), which has often been criticized for inconsistencies between the theoretical and the empirical model. A potential alternative to understand the structure of psychological well-being is network models, which conceptualizes psychological phenomena as emerging systems of mutually connected indicators. We examined the network structure of the Spanish 29-item PWBS in a sample of 1,404 adults. We estimated a regularized partial correlation network using the graphical LASSO algorithm in the item and dimension level. We tested the stability of both networks and identified the most important variables of the network. The PWBS network model revealed four dimensions, with self-acceptance, life purpose and environmental mastery clustering together. Node strength centrality suggested that self-acceptance is the most central dimension in the psychological well-being structure as measured by the PWBS. Despite the network model of psychological well-being did not replicate the theoretical structure of Ryff’s model, it provides a novel conceptualization of psychological well-being and proposes target indicators for mental health interventions.

scholarly journals Internalizing symptoms, well-being, and correlates in adolescence: A multiverse exploration via cross-lagged panel network models

2021 ◽  
pp. 1-15
Author(s):  
Louise Black ◽  
Margarita Panayiotou ◽  
Neil Humphrey
Keyword(s):  
Mental Health ◽  
Internalizing Symptoms ◽  
Large Scale ◽  
Community Sample ◽  
Social Indicators ◽  
Network Models ◽  
Well Being ◽  
The Stability ◽  
Similar Complex ◽  
People Today

Abstract Internalizing symptoms are the most prevalent mental health problem in adolescents, with sharp increases seen, particularly for girls, and evidence that young people today report more problems than previous generations. It is therefore critical to measure and monitor these states on a large scale and consider correlates. We used novel panel network methodology to explore relationships between internalizing symptoms, well-being, and inter/intrapersonal indicators. A multiverse design was used with 32 conditions to consider the stability of results across arbitrary researcher decisions in a large community sample over three years (N = 15,843, aged 11–12 at Time 1). Networks were consistently similar for girls and boys. Stable trait-like effects within anxiety, attentional, and social indicators were found. Within-person networks were densely connected and suggested mental health and inter/intrapersonal correlates related to one another in similar complex ways. The multiverse design suggested the particular operationalization of items can substantially influence conclusions. Nevertheless, indicators such as thinking clearly, unhappiness, dealing with stress, and worry showed more consistent centrality, suggesting these indicators may play particularly important roles in the development of mental health in adolescence.

scholarly journals Black women and COVID-19: The need for targeted mental health research and practice

2021 ◽  
Vol 20 (1-2) ◽  
pp. 247-255
Author(s):  
Quenette L Walton ◽  
Rosalyn Denise Campbell ◽  
Joan M Blakey
Keyword(s):  
Mental Health ◽  
Social Work ◽  
Black Women ◽  
Well Being ◽  
Mental Health Research ◽  
Women’S Mental Health ◽  
Frontline Workers ◽  
Women's Mental Health ◽  
Personal Experiences ◽  
Health And Well Being

COVID-19 has significantly impacted a substantial number of Black Americans. Black women, in particular, are facing challenges financially, physically, and mentally during this unprecedented time. Between serving as frontline workers, being concerned about contracting the virus, contributing to their families financially, and worrying about their loved ones’ health, Black women are experiencing great strain on their mental health and well-being. These stressors illustrate the need for social work researchers and practitioners to address Black women’s mental health. This paper presents our reflections, experiences, and response to COVID-19 as Black women and scholars. Guided by our reflections and personal experiences, we put forth suggestions and reflexive thoughts for social work researchers and practitioners to prioritize Black women’s mental health during and after these unprecedented times.

scholarly journals Mental Health and Psychological Well-Being Among Management Students and Educators

2021 ◽  
Vol 45 (1) ◽  
pp. 3-18
Author(s):  
Marissa S. Edwards ◽  
Angela J. Martin ◽  
Neal M. Ashkanasy
Keyword(s):  
Mental Health ◽  
Well Being ◽  
Psychological Well Being ◽  
Management Students

Mental Health and Well-Being Among Individuals With a Sensory Loss During COVID-19 Lockdown Measures

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 409-409
Author(s):  
Natascha Merten ◽  
Amy Schultz ◽  
Matthew Walsh ◽  
Suzanne van Landingham ◽  
Paul Peppard ◽  
...  
Keyword(s):  
Mental Health ◽  
Hearing Impairment ◽  
Mental Health Problems ◽  
Well Being ◽  
Vision Impairment ◽  
Sensory Loss ◽  
Future Research ◽  
Sensory Impairment ◽  
Psychological Well Being ◽  
Health And Well Being

Abstract Hearing and vision impairment are highly prevalent chronic conditions and are associated with poorer mental health and well-being. Mental health problems may be exacerbated by COVID-19-related lockdown measures and limitations of in-person contacts may affect those with sensory impairments more severely. We aimed to determine whether hearing and/or visual impairment were associated with worse mental health and psychological well-being during lockdown measures in Spring/Summer 2020 in Wisconsin. We included 1341 (64% women, aged 20-92 years) Survey of the Health of Wisconsin participants of a COVID-19 survey (May-June, 2020). We assessed self-reported current mental health and psychological well-being and vision and hearing impairment. Logistic regression models with vision and hearing impairments as determinants and multiple mental health and well-being outcomes were used and adjusted for age, gender, race, education, heart disease, hypertension, hyperlipidemia and diabetes. In preliminary analyses, we found associations of vision impairment with increased odds of generalized anxiety disorder (odds ratio=2.10; 95% confidence interval=1.32-3.29) and depression (2.57; 1.58-4.11). Individuals with a vision impairment were more likely to be taking medication for depression (1.75; 1.13-2.68), report being lonely (1.65; 1.00-2.64) and report hopelessness (1.45; 1.01-2.08). Individuals with a hearing impairment were more likely to be taking depression medications (1.72; 1.07-2.73) and to report being lonely (1.80; 1.05-2.98). Sensory impairment was not associated with stress levels or sense of purpose in life. Individuals with sensory impairment may represent a particularly vulnerable population during the COVID-19 pandemic. Future research should determine underlying reasons and interventions to mitigate this populations’ disadvantages.

scholarly journals Depression and Psychological Well-Being as Distinct Constructs: Mutually Exclusive Associations With Biomarker

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 251-251
Author(s):  
Kheng Siang Ted Ng ◽  
Shu Cheng Wong ◽  
Glenn Wong ◽  
Ee Heok Kua ◽  
Anis Larbi ◽  
...  
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Depressive Symptoms ◽  
Factor Score ◽  
Well Being ◽  
Community Dwelling ◽  
The Other ◽  
Factor Scores ◽  
Psychological Well Being ◽  
Wide Range

Abstract Despite increasing emphasis on assessing the mental health of older adults, there has been inconclusive evidence on whether depression and psychological well-being (PWB) are fundamentally distinct constructs or representations of the opposite ends of the mental health spectrum. To instantiate either hypothesis, investigation of the associations between mental health scales and biomarkers have been proposed. First, we assessed depressive symptoms and PWB in community-dwelling older adults (N=59, mean age=67) using the Self-Rating Depression Scale (SDS) and Ryff’s Scale of PWB (comprising six sub-scales). We measured a wide range of immune markers employing ELISA and flow cytometry. Subsequently, we used principal component analysis (PCA) to aggregate and derived biomarker factor scores. Lastly, multiple linear regressions were performed to examine the associations between the scales and the derived biomarker factor scores, controlling for covariates. PCA extracted six biomarker factors. Biomarker factor score 1 was significantly associated with PWB (β=-0.029, p=0.035) and the PWB sub-scale, self-acceptance (β=-0.089, p=0.047), while biomarker factor score 4 was significantly associated with the PWB sub-scale, purpose in life (β=-0.087, p=0.025). On the other hand, biomarker factor 6 was significantly associated with SDS (β=-0.070, p=0.008). There were mutually- exclusive associations between the scales with biomarker factor scores, supporting the hypothesis of distinct constructs. Our findings expanded the biomarkers of depression and PWB, deepening understanding of the biological underpinnings of depressive symptoms and PWB. These findings have implications in field work, since researchers could not infer one construct from the other, the examination of both constructs are essential.

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

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