scholarly journals Shame-Proneness Predicts Social Psychopathology: Considering the Sociometer Theory of Shame

2021 ◽  
Author(s):  
Thomas Carpenter ◽  
Thane Erickson ◽  
Oxana Stebbins ◽  
Kylie Fraga
Keyword(s):  
Social Anxiety ◽  
Negative Affect ◽  
Depressive Disorders ◽  
Psychosocial Stressors ◽  
Social Evaluation ◽  
Emotional Symptoms ◽  
Total N ◽  
Shame Proneness ◽  
Guilt Proneness ◽  
Sociometer Theory

Introduction: The classic act-person model of shame-proneness defined shame as originating from negative self-appraisals following wrongful actions, conferring broad vulnerability to psychopathology. However, recent developments postulate that shame may originate from real or imagined social evaluation (sociometer view of shame). If so, shame might leave one vulnerable to psychosocial stressors and may manifest in social anxiety specifically, even after accounting for general negative affect. We investigated how shame-proneness predicted concurrent symptoms and prospective responses to interpersonal stressors (social anxiety, feeling evaluated, and other symptoms) over five weeks in a sample including individuals at clinical and subclinical levels of emotional symptoms. Method: We oversampled for individuals meeting criteria for anxiety and depressive disorders based on clinical interviews (n = 58) and also included those not meeting diagnostic criteria (n = 101) to ensure a broad range of symptoms (total N = 159). Participants completed baseline measures of shame- and guilt-proneness, trait negative affect (NA), and symptoms of social anxiety, depression, and generalized anxiety disorders followed by symptom diaries for 5 weeks following their worst psychosocial stressors (1,923 diaries). Results: As expected, even after controlling for NA and guilt-proneness, shame-proneness uniquely predicted concurrent social anxiety (∆R2 = 8%) and prospectively predicted experiences of social evaluation. Shame-proneness demonstrated weaker links to depression, and no unique links to general anxiety and worry. Discussion: Shame-proneness functioned in a manner predicted by sociometer theory, demonstrating specificity for social evaluative symptoms and concerns. Results have implication both for shame theory and clinical practice with shame-prone individuals.

Machine Learning in Mental Health: Recognizing the Symptoms of Depressive and Anxiety Disorders

2021 ◽  
Author(s):  
Olga Troitskaya ◽  
Andrey Zakharov
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Depressive Disorders ◽  
Learning Technologies ◽  
Emotional Symptoms ◽  
Full Spectrum ◽  
Large Classes ◽  
Machine Learning Model ◽  
Self Talk ◽  
Potential Possibility

Machine learning technologies can be used to extract important information about mental health of individuals from unstructured texts, including social media posts and transcriptions of counselling sessions. So far machines have been trained to detect the presence of mental disorder, but they still need to learn to recognize individual symptoms in order to make a valid diagnosis. This study presents an attempt to train a machine learning model to recognize individual symptoms of anxiety and depressive disorders. We collected 1065 posts about depression and anxiety from online psychological forums; divided messages into 7149 replicas and classified each replica according to the DSM-5 criteria. We found that users mention emotional symptoms far more often than physical ones. An imbalanced dataset did not allow us to recognize the full spectrum of symptoms with sufficient accuracy. A two-stage model was developed: at the first stage the model recognized large classes of depression, anxiety or irritability. At the second stage it recognized sub-classes of symptoms, such as depressed mood, suicidal intent and negative self-talk within the depression class; and excessive worry and social anxiety within the anxiety class. The research has demonstrated the potential possibility of extracting symptoms of mental disorders from unstructured data on a larger dataset.

scholarly journals Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

10.2196/16875 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16875 ◽  
Author(s):  
Nicholas C Jacobson ◽  
Berta Summers ◽  
Sabine Wilhelm
Keyword(s):  
Social Anxiety ◽  
Negative Affect ◽  
Positive Affect ◽  
Symptom Severity ◽  
Anxiety Symptom ◽  
Self Report ◽  
Sensor Data ◽  
Digital Phenotyping ◽  
Passive Sensor ◽  
Digital Biomarkers

Background Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. Objective This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. Methods In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. Results The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. Conclusions These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

scholarly journals Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors (Preprint)

2019 ◽  
Author(s):  
Nicholas C Jacobson ◽  
Berta Summers ◽  
Sabine Wilhelm
Keyword(s):  
Social Anxiety ◽  
Negative Affect ◽  
Positive Affect ◽  
Symptom Severity ◽  
Anxiety Symptom ◽  
Self Report ◽  
Sensor Data ◽  
Digital Phenotyping ◽  
Passive Sensor ◽  
Digital Biomarkers

BACKGROUND Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. OBJECTIVE This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. METHODS In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants’ social anxiety symptom severity. RESULTS The results suggested that these passive sensor data could be utilized to accurately predict participants’ social anxiety symptom severity (<i>r</i>=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. CONCLUSIONS These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.

Distress after a Single Violent Crime: How Shame-Proneness and Event-Related Shame Work Together as Risk Factors for Post-Victimization Symptoms

2011 ◽  
Vol 109 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Olof Semb ◽  
Lotta M.J. Strömsten ◽  
Elisabet Sundbom ◽  
Per Fransson ◽  
Mikael Henningsson
Keyword(s):  
Risk Factors ◽  
Violent Crime ◽  
Structural Equation ◽  
Equation Modeling ◽  
Mediating Role ◽  
Shame Proneness ◽  
Shame And Guilt ◽  
Potential Risk Factors ◽  
Guilt Proneness

To increase understanding of post-victimization symptom development, the present study investigated the role of shame- and guilt-proneness and event-related shame and guilt as potential risk factors. 35 individuals ( M age = 31.7 yr.; 48.5% women), recently victimized by a single event of severe violent crime, were assessed regarding shame- and guilt-proneness, event-related shame and guilt, and post-victimization symptoms. The mediating role of event-related shame was investigated with structural equation modeling (SEM), using bootstrapping. The guilt measures were unrelated to each other and to post-victimization symptoms. The shame measures were highly intercorrelated and were both positively correlated to more severe post-victimization symptom levels. Event-related shame as mediator between shame-proneness and post-victimization symptoms was demonstrated by prevalent significant indirect effects. Both shame measures are potent risk factors for distress after victimization, whereby part of the effect of shame-proneness on post-victimization symptoms is explained by event-related shame.

scholarly journals A Coordinated Analysis of Variance in Affect in Daily Life

Assessment ◽  
2018 ◽  
Vol 27 (8) ◽  
pp. 1683-1698 ◽  
Author(s):  
Stacey B. Scott ◽  
Martin J. Sliwinski ◽  
Matthew Zawadzki ◽  
Robert S. Stawski ◽  
Jinhyuk Kim ◽  
...  
Keyword(s):  
Negative Affect ◽  
Positive Affect ◽  
Ecological Momentary Assessment ◽  
Daily Life ◽  
Daily Diary ◽  
Levels Of Analysis ◽  
Total N ◽  
Ecological Momentary ◽  
Momentary Assessment ◽  
Widespread Interest

Despite widespread interest in variance in affect, basic questions remain pertaining to the relative proportions of between-person and within-person variance, the contribution of days and moments, and the reliability of these estimates. We addressed these questions by decomposing negative affect and positive affect variance across three levels (person, day, moment), and calculating reliability using a coordinated analysis of seven daily diary, ecological momentary assessment (EMA), and diary-EMA hybrid studies (across studies age = 18-84 years, total Npersons = 2,103, total Nobservations = 45,065). Across studies, within-person variance was sizeable (negative affect: 45% to 66%, positive affect: 25% to 74%); in EMA more within-person variance was attributable to momentary rather than daily level. Reliability was adequate to high at all levels of analysis (within-person: .73-.91; between-person: .96-1.00) despite different items and designs. We discuss the implications of these results for the design of future intensive studies of affect variance.

Prevalence of Premenstrial Disphoric Disorder Associated Factors among Students of Tabor Secondary and Preparatory School in Hawassa City, Ethiopia Cross Sectional

2020 ◽  
Author(s):  
Mulugeta Gobena Tadesse ◽  
Dereje Dirago Dire ◽  
Yacob Yacob Abraham
Keyword(s):  
Associated Factors ◽  
Depressive Disorders ◽  
Premenstrual Dysphoric Disorder ◽  
Emotional Symptoms ◽  
Cross Sectional ◽  
Preparatory School ◽  
Statistical Manual ◽  
Diagnostic And Statistical Manual ◽  
History Of ◽  
Menstruating Women

Abstract Background: Premenstrual dysphoric disorder (PMDD)-is a severe and disabling form of premenstrual Syndrome affecting 3-8% of menstruating women. The disorder consists of a cluster of affective, behavioral and somatic symptoms that recur monthly during the luteal phase the menstrual cycle. Premenstrual dysphoric disorder (PMDD) was added to the list of depressive disorders in the diagnostic and statistical manual of mental disorders in 2013. The exact pathogenesis of the disorder is still unclear.Objective: To assess the prevalence of PMDD and its associated factors among students of Hawassa tabor secondary and preparatory school.Method: A cross sectional institutional based was conducted among 351 randomly selected female students of Hawassa tabor school. Data was collected by three students were facilitate the works with closed ended structured questionnaire and they was trained on how to collect the data. The collected data was entered, analyzed and cleaned by SPS.Results: prevalence of premenstrual dysphoric disorder in this study was 76.9%. Of each symptom is more than ninety present or 324 (92.3%) respondents can’t have experience unpleasant physical or emotional symptoms peculiar to the five days before the onset of menses & 27(7.7%) participants have show the symptoms. Among those 26 (7.4%) have present for the past ≥3 consecutive cycles. 46 (13.1%) have family history of such symptoms.Conclusions: These findings have implications for both women and medical providers, who should be aware that PMS symptoms are prevalent and often distressing, yet also understand that the severity of symptoms may remit over time.

The Prevalence of Mental Disorders among Clergy in São Paulo, Brazil

1996 ◽  
Vol 24 (4) ◽  
pp. 313-322 ◽  
Author(s):  
Francisco Lotufo-Neto
Keyword(s):  
Mental Disorders ◽  
Religious Orientation ◽  
Depressive Disorders ◽  
Diagnostic Interview ◽  
The Self ◽  
Self Report ◽  
Psychosocial Stressors ◽  
The Church ◽  
Life Inventory ◽  
Psychiatric Screening

To investigate their mental disorders prevalence, the Self-Report Psychiatric Screening Questionnaire (SRQ-20) and the Religious Life Inventory were mailed to 750 religious ministers. From the 207 who answered, 40 were randomly chosen and invited to a diagnostic interview using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) and an open interview using the Severity of Psychosocial Stressors Scale (DSM-III-R Axis IV). During the month before the interview, mental disorders prevalence was 12.5%, and 47% received a psychiatric diagnosis when the lifetime period was considered. Their main diagnoses were Depressive Disorders (16.4%), Sleep Disorders (12.9%) and Anxiety Disorders (9.4%). Intrinsic religious orientation was associated with positive mental health, and quest orientation scores were significantly higher in the group with a larger probability of mental disorder symptoms and diagnoses. Financial problems, problems with church members and with other pastors, leadership conflicts, marital difficulties, doctrinal problems in the church, and overwork were the main identified stressors.

scholarly journals Distinct Risk Profiles in Social Anxiety Disorder

2020 ◽  
Vol 8 (3) ◽  
pp. 477-490
Author(s):  
Esther S. Tung ◽  
Timothy A. Brown
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Depressive Disorders ◽  
Clinical Expression ◽  
Risk Profiles ◽  
Factor Mixture Model ◽  
Class 1 ◽  
Severity Class ◽  
Initial Support

Using a factor mixture model (FMM) approach, we examined whether social anxiety disorder (SAD) could be subtyped by distinct risk profiles and whether these subtypes predicted different manifestations of the disorder. We derived risk profiles from neurotic temperament (NT), positive temperament (PT), and autonomic arousability (AA), which are hypothesized to be important in the maintenance of anxiety disorders such as SAD. In our sample of 758 SAD outpatients, a two-class FMM solution fit the data best. Class 1 was characterized by very low PT, whereas PT in Class 2 was substantially higher. The two classes differed to a lesser extent on NT but were virtually equivalent on AA. Class 1 had significantly more men and individuals with depressive disorders, generalized SAD, and higher SAD severity. Class 2 had more individuals with performance subtype SAD. These findings provide initial support for distinct risk profiles within SAD that may be predictive of its clinical expression.

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