The impact of outcome expectancy on therapy outcome in adolescents with borderline personality disorder

Bäumer, Anna-Valeska; Fürer, Lukas; Birkenberger, Carolin; Wyssen, Andrea; Steppan, Martin; Zimmermann, Ronan; Gaab, Jens; Kaess, Michael; Schmeck, Klaus

doi:10.1186/s40479-022-00200-1

Cited by 3 publications

(2 citation statements)

References 87 publications

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“…Clinically speaking, a patient's omission of these courtesy smiles at a session's onset is not merely indicative of "poor etiquette." It might instead signify a diminished outcome expectation from the patient's end, a factor we have identified as crucial in the treatment of BPD [48]. Future studies should explore the evolution of such courteous behaviors over time and their transformation during psychotherapeutic treatment.…”

Section: Discussionmentioning

confidence: 97%

“…This pattern may be attributed to the widespread social convention of beginning interactions with courtesy smiles or “social smiling.” Intriguingly, patients who eventually drop out (represented by the red line) seem less inclined to adhere to this convention compared to the other groups. Clinically speaking, a patient’s omission of these courtesy smiles at a session’s onset is not merely indicative of “poor etiquette.” It might instead signify a diminished outcome expectation from the patient’s end, a factor we have identified as crucial in the treatment of BPD [48]. Future studies should explore the evolution of such courteous behaviors over time and their transformation during psychotherapeutic treatment.…”

Section: Discussionmentioning

confidence: 99%

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Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study

Steppan,

Zimmermann,

Fürer

et al. 2023

Psychopathology

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Background: New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g., the Facial Action Coding System), is time-consuming. Purpose: We tested whether this technology can reliably identify in-session emotional expression in a naturalistic treatment setting, and how these measures relate to the outcome of psychotherapy. Method: We applied a machine learning emotion classifier to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We validated the findings with human ratings according to the Clients Emotional Arousal Scale (CEAS) and explored associations with treatment outcomes. Results: Overall, machine learning ratings showed significant agreement with human ratings. Machine learning emotion classifiers, particularly the display of positive emotions (smiling and happiness), showed medium effect size on median-split treatment outcome (d = 0.3) as well as continuous improvement (r = 0.49, p < 0.05). Patients who dropped out form psychotherapy, showed significantly more neutral expressions, and generally less social smiling, particularly at the beginning of psychotherapeutic sessions. Conclusions: Machine learning classifiers are a highly promising resource for research in psychotherapy. The results highlight differential associations of displayed positive and negative feelings with treatment outcomes. Machine learning emotion recognition may be used for the early identification of drop-out risks and clinically relevant interactions in psychotherapy.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study

Steppan,

Zimmermann,

Fürer

et al. 2023

Psychopathology

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Personality disorders and their association with adverse childhood events

Crişan,

Nechita

2024

Current Opinion in Psychiatry

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Purpose of review Theoretical models of personality disorders (PDs) highlight the role of adverse childhood experiences (ACEs) in the development and maintenance of personality pathology. This review presents recent studies on the associations between childhood adversity and PDs, while discussing mechanisms and factors that impact these relationships. Recent findings Studies point to small to moderate associations between childhood adversity and cluster A, cluster B and cluster C PDs, and some research suggests that childhood adversity could be a predictor for the onset and development of personality pathology. Most research focuses on borderline PD, while research on other PDs is limited. Some mechanisms that explain these associations have been proposed, with the literature investigating emotion dysregulation, dissociation, avoidance or psychosocial dysfunction as mediators. Summary The consistent associations between ACEs and PDs seems to point that childhood adversity could be a transdiagnostic risk factor for PDs, but research warrants further prospective investigations for causal conclusions, and calls for future research to investigate potential mediators and moderators more thoroughly.

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Culture and ICD-11 personality disorder: Implications for clinical practice across diverse ethnic groups

Hualparuca-Olivera,

Caycho-Rodríguez,

Torales

et al. 2024

Int J Soc Psychiatry

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Background: Personality disorder (PD) in ICD-11 is defined primarily by self and interpersonal dysfunction and optionally by other qualifiers. This definition is inseparable from relativism of cultural determinants. Aims: This review aimed to synthesize the relevant aspects of the influence of culture on clinical practice and health management for this condition, aligning them to the ICD-11 PD model. Method: In Scopus, we systematically searched for studies that included the text strings: cultur* | personality AND (disorder* OR patholog*) without any restrictions on publication date or language or other exclusion criteria, up to November 2022. Results: Evidence suggests that cultural variables in ethnic groups (Western and non-Western) such as the individualist/collectivist philosophy, historical/generational trauma, immigration, acculturation, religion, and gender influence the etiology, semiology, epidemiology, evaluation, diagnosis, treatment, and management of health services for ICD-11 PD. We discuss the limitations and propose future lines of research on this topic based on our knowledge and experience. In this review, we provide the scientific community and clinicians with relevant cultural information to guide their practice and propose strategies to manage PD from the ICD-11 model. Conclusions: More research is needed using mixed study methodologies on stigma, the experiences of patients, clinicians, and health agencies, to reduce the care gaps and achieve a culturally comprehensive, inclusive, and competent use of this new model.

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The impact of outcome expectancy on therapy outcome in adolescents with borderline personality disorder

Cited by 3 publications

References 87 publications

Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study

Machine Learning Facial Emotion Classifiers in Psychotherapy Research: A Proof-of-Concept Study

Personality disorders and their association with adverse childhood events

Culture and ICD-11 personality disorder: Implications for clinical practice across diverse ethnic groups

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