Modifying mental health help-seeking stigma among undergraduates with untreated psychiatric disorders: A pilot randomized trial of a novel cognitive bias modification intervention

Stanley, Ian H.; Hom, Melanie A.; Joiner, Thomas E.

doi:10.1016/j.brat.2018.01.008

Cited by 14 publications

(11 citation statements)

References 47 publications

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“…28,38 Reupert et al 39 utilized a competence enhancement model, whereas Griffiths et al 40 employed the social anxiety help-seeking behavior framework. A few others incorporated cognitive-behavior principles [41][42][43] and the cognitive bias modification paradigm 27 to develop the intervention. King et al 38 used the motivational interviewing approach, whereas Suka et al 34 based their intervention on theories of persuasion, decision-making, and attitude-behavior relations.…”

Section: Discussionmentioning

confidence: 99%

Technology-Based Interventions to Improve Help-Seeking for Mental Health Concerns: A Systematic Review

Johnson

Sanghvi

Mehrotra

2021

Indian Journal of Psychological Medicine

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Background: Despite the high prevalence of mental health disorders worldwide, a significant proportion of distressed individuals do not seek professional help. Digital technology can be a potential bridge to reduce the treatment gap for mental disorders. A systematic review was undertaken to examine the technology-based interventions aimed at improving help-seeking attitude, intention, or behavior for mental health concerns. Methods: The literature search was conducted in January–February 2020 through various e-databases using relevant keywords that targeted help-seeking interventions for mental health disorders via different technology modes. Results: 21 studies (15 randomized controlled trials and six non-randomized studies) were reviewed. The included studies were published between April 2006 to February 2020. Majority of the interventions led to an increase in the help-seeking variables. The crucial role of online delivery, participant involvement, and embedded links to professional services in encouraging help-seeking is highlighted. The review emphasizes the need for understanding utility of multicomponent interventions with personalized elements targeting help-seeking behavior, particularly in low-middle-income countries, and studies involving longer duration follow-ups. Conclusion: This systematic review is the first of its kind to examine technology-based interventions to improve help-seeking for mental health and suggests that such interventions play a crucial role in positively impacting help-seeking. The complex interplay between the relevant variables such as mental health literacy, stigma, help-seeking attitude, intention and behavior, and the intervention components that may have a differential bearing on these variables are issues that merit urgent attention in further research.

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Section: Discussionmentioning

confidence: 99%

Technology-Based Interventions to Improve Help-Seeking for Mental Health Concerns: A Systematic Review

Johnson

Sanghvi

Mehrotra

2021

Indian Journal of Psychological Medicine

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“…In another study, a computer-based intervention designed to alter maladaptive help-seeking self-stigma cognitions did not reduce self-stigma relative to a comparison condition among undergraduates diagnosed with a Diagnostic and Statistical Manual of Mental Disorders (5th ed. ; American Psychiatric Association, 2013) psychiatric disorder who had not received treatment in the past year (Stanley, Hom, & Joiner, 2018). However, lack of differences between the groups may be partially due to the high-quality comparison group, which contained activities similar to those used in some help-seeking self-stigma interventions; participants reviewed of psychoeducation material designed to increase mental health literacy, reduce stigma, and explain treatment options.…”

mentioning

confidence: 99%

Seeking help despite the stigma: Experiential avoidance as a moderated mediator.

Brenner¹,

Cornish²,

Heath³

et al. 2020

Journal of Counseling Psychology

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The help-seeking literature identifies a model wherein public stigma of seeking help is internalized as self-stigma of seeking help, which, in turn, decreases help-seeking outcomes. The current study considered whether experiential avoidance, or a tendency to avoid painful thoughts or emotions, moderates how strongly these stigmata relate to help-seeking intentions among university students. Specifically, this study tested whether experiential avoidance moderates (a) the direct relationship between self-stigma of seeking psychological help and help-seeking intentions and (b) the indirect relationship between public stigma and help-seeking intentions. Conditional process modeling in a university student sample (N = 235) supported these hypotheses. The direct relationship between self-stigma and help-seeking intentions was nonsignificant and weaker for those who reported low experiential avoidance than for those who reported high experiential avoidance. Results also demonstrated a moderated indirect effect wherein the relationship between self-stigma and intentions was nonsignificant among those reporting low levels of experiential avoidance. This suggests that self-stigma may predict help-seeking intentions when avoidance of therapy functions as a means for avoiding unpleasant emotions. These findings suggest that interventions designed to decrease experiential avoidance by increasing openness to unpleasant emotions may offer a novel avenue to attenuate the impact of self-stigma on help-seeking intentions without requiring the difficult task of reducing stigma altogether.

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“…Personal characteristics like internet addiction and alcohol consumption are also considered in this study. In previous literature, it has been shown that social environment, socioeconomic status, demographic characters, family background, and personal characteristics are significantly related to anxiety and depressive disorders among college students [32,4,30,1,18,16]. We try to examine the data related to anxiety and depressive disorder through the factors responsible for the mental health illness.…”

Section: Factors Of Mental Health Illnessmentioning

confidence: 99%

“…A person who has been diagnosed with persistent depressive disorder may have major depressive episodes along with periods of less severe symptoms, the cycle of these symptoms lasts for two years, making this disorder more chronic. A large number of students usually suffer from persistent depressive disorder, though there is a likely chance that consistent stress and anxiety leads to this depression making it undiagnosable at an early stage [30]. Psychotic depression differs from persistent depressive disorder, where a person has severe depression in form of psychosis, where symptoms include having disturbing false fixed beliefs and an imaginary sense of hearing or seeing things that others are unable to hear or see.…”

Section: Depressive Disordermentioning

confidence: 99%

An Empirical Comparison of Machine Learning Models for Student’s Mental Health Illness Assessment

Muzumdar

Basyal

Vyas

2022

AJCIS

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Student’s mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student’s mental health illness.

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Modifying mental health help-seeking stigma among undergraduates with untreated psychiatric disorders: A pilot randomized trial of a novel cognitive bias modification intervention

Cited by 14 publications

References 47 publications

Technology-Based Interventions to Improve Help-Seeking for Mental Health Concerns: A Systematic Review

Technology-Based Interventions to Improve Help-Seeking for Mental Health Concerns: A Systematic Review

Seeking help despite the stigma: Experiential avoidance as a moderated mediator.

An Empirical Comparison of Machine Learning Models for Student’s Mental Health Illness Assessment

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