Therapeutic Feedback with the MMPI-2

Levak, Richard W.; Siegel, Liza; Nichols, David S.

doi:10.4324/9780203828908

Cited by 5 publications

(5 citation statements)

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“…Nevertheless, when considering that most previous studies only utilized ASD patient factors as associated/predictive factors for parental distress (Scibelli et al, 2021;Mello et al, 2022), our study provided a new insight into the understanding of parental distress by utilizing explainable ML models with model features related to caregivers' psychopathology. However, interpretation needs caution since MMPI-2 clinical scales should not be independently addressed (Levak et al, 2012). A comprehensive approach for significant MMPI-2 clinical scales might be appropriate.…”

Section: Discussionmentioning

confidence: 99%

“…That is, the caregivers' personality traits also significantly affect how they perceive difficulty in raising their child. When considering a caregiver of the 2-0 code type, for example, one may be challenged by their child's aspects that make one feels tough to raise, such as aggressive behavior, because these people tend to represent chronic depression, guilty, social withdrawal, and lack of confidence (Levak et al, 2012). Additionally, higher scale scores of 2-0 code type are clinically associated with unipolar depression or remitted depression, and pessimism, the negative cognition of which might lead to a bias that affects measuring a child as a more difficult child (Wetzler et al, 1995;Suzuki et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

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Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models

Choi,

Kim,

Kim

et al. 2023

Front. Neurosci.

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IntroductionPrevious studies have investigated predictive factors for parenting stress in caregivers of autism spectrum disorder (ASD) patients using traditional statistical approaches, but their study settings and results were inconsistent. Herein, this study aimed to identify major predictors for parenting stress in this population by developing explainable machine learning models.MethodsStudy participants were collected from the Department of Child and Adolescent Psychiatry, Severance Hospital, Yonsei University College of Medicine, Seoul, the Republic of Korea between March 2016 and October 2020. A total of 36 model features were used, which include subscales of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) for caregivers’ psychopathology, Social Responsiveness Scale-2 for core symptoms, and Child Behavior Checklist (CBCL) for behavioral problems. Machine learning classifiers [eXtreme Gradient Boosting (XGBoost), random forest (RF), logistic regression, and support vector machine (SVM) classifier] were generated to predict severe total parenting stress and its subscales (parental distress, parent-child dysfunctional interaction, and difficult child). Model performance was assessed by area under the receiver operating curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. We utilized the SHapley Additive exPlanations tree explainer to investigate major predictors.ResultsA total of 496 participants were included [mean age of ASD patients 6.39 (SD 2.24); 413 men (83.3%)]. The best-performing models achieved an AUC of 0.831 (RF model; 95% CI 0.740–0.910) for parental distress, 0.814 (SVM model; 95% CI 0.720–0.896) for parent-child dysfunctional interaction, 0.813 (RF model; 95% CI 0.724–0.891) for difficult child, and 0.862 (RF model; 95% CI 0.783–0.930) for total parenting stress on the test set. For the total parenting stress, ASD patients’ aggressive behavior and anxious/depressed, and caregivers’ depression, social introversion, and psychasthenia were the top 5 leading predictors.ConclusionBy using explainable machine learning models (XGBoost and RF), we investigated major predictors for each subscale of the parenting stress index in caregivers of ASD patients. Identified predictors for parenting stress in this population might help alert clinicians whether a caregiver is at a high risk of experiencing severe parenting stress and if so, providing timely interventions, which could eventually improve the treatment outcome for ASD patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models

Choi,

Kim,

Kim

et al. 2023

Front. Neurosci.

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“…Since its publication in 1989, the MMPI-2 has been employed in over 2,000 research papers (Graham, 2015). Thus, the MMPI-2 is a tool with a well-established and empirically supported research tradition (Butcher et al, 2004;Graham, 2015;Levak et al, 2011). It is suitable for the diagnosis of psychopathologies and personality disorders.…”

Section: Persistent Delusional Disorder Assessed By Mmpi-2mentioning

confidence: 99%

Application of the MMPI-2 inventory in the differential diagnosis of psychotic disorders – based on profile analysis of a patient diagnosed with persistent delusional disorder and personality disorder

Jaskólski,

Izydorczyk

2023

Psychiatr Psychol Klin

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Aim: Paranoid states or traits can be present in various mental disorders; hence, their clarification is valuable for establishing a reliable nosological diagnosis. Clinical practice and literature on the subject highlight a range of defence mechanisms specific to the discussed nosological group. These mechanisms can impede the process of psychiatric and psychotherapeutic diagnosis and treatment. In contemporary evidence-based medical practice, the prevalent clinical approach is based on qualitative methods (e.g. free interviews and observations) complemented by standardised diagnostic tools. This approach assists the clinician in supplementing, organising, and objectifying clinical data related to the patient’s mental state. It is particularly beneficial for handling data that may be uncertain or unavailable in an observational study for various reasons. The article presents insightful and pragmatic theoretical, empirical, and case-related information on the potential application of the Minnesota Multiphasic Personality Inventory (MMPI-2) in the psychological diagnosis of patients with persistent delusional disorders. Methods: The paper explores theoretical issues and analyses a case study involving a female patient with a suspected diagnosis of delusional disorder and co-occurring personality disorder. In the case study, the authors employed qualitative methods, including observation and free psychological interview, and supplemented clinical data with insights gained through the use of a standardised psychometric method: the MMPI-2. Reflections and practical recommendations are divided into two groups. One group comprises the potential applications of the MMPI-2 in clinical psychological diagnosis. The other focuses on the importance of the diagnostic alliance and its roles in the process of planning the psychological diagnosis in patients with delusional disorders.

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“…Therefore, patient factors associated with adherence in patients with psychiatric disorders, including depressive disorder (DD), BD, and anxiety disorder (AD), were identified in the present study. In addition, personality profile were assessed using the Minnesota Multiphasic Personality Inventory (MMPI), reflects understanding of personality and psychopathology [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Patient Factors Influencing Outpatient Retention in Patients with Affective and Anxiety Disorders: A Retrospective Study

Chang

Woo

Wang

et al. 2021

Clin Psychopharmacol Neurosci

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The aim of the present study is to identify the factors that affect retention in outpatients with psychiatric disorders as indicators of treatment adherence, including Minnesota Multiphasic Personality Inventory (MMPI) scores. Methods: The medical records of 146 patients diagnosed with major depressive disorder, bipolar disorder, or anxiety disorder for at least 10 years and discharged were retrospectively reviewed in the present study. The subjects were categorized based on the duration of outpatient treatment as ＜ 6 months (L6) or ≥ 6 months (M6) groups and reclassified as ＜ 36 months (L36) and ≥ 36 months (M36) groups. The demographic, clinical, and personality characteristics of the groups were compared. Results: Patients in M6 and M36 groups were more likely to have a higher educational level compared with those in the L6 and L36 groups, respectively. Patients in the M6 group showed significantly lower hypomania (Ma) scores on the MMPI test than did patients in the L6 group. Conclusion: The association between high Ma score on the MMPI test and early discontinuation of treatment suggests that impulsivity, hostility, and disinhibition confer higher risk of nonadherence.

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Therapeutic Feedback with the MMPI-2

Cited by 5 publications

References 0 publications

Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models

Identifying major predictors for parenting stress in a caregiver of autism spectrum disorder using machine learning models

Application of the MMPI-2 inventory in the differential diagnosis of psychotic disorders – based on profile analysis of a patient diagnosed with persistent delusional disorder and personality disorder

Patient Factors Influencing Outpatient Retention in Patients with Affective and Anxiety Disorders: A Retrospective Study

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