Introduction of the generic sense of ability to adapt scale and validation in a sample of outpatient adults with mental health problems

IntroductionThe ability to adapt is a core aspect of daily human life. Recent models and theories emphasize its essential role for health and well-being. It concerns the perceived ability to readjust and actively deal with the psychosocial consequences of challenging events. While many questionnaires measure competences related to adaptability to specific conditions, a scale that measures a generic sense of the ability to adapt is lacking. The aim of the present study is to introduce the Generic Sense of Ability to Adapt Scale (GSAAS) and to examine its psychometric properties.MethodsThe article describes two sub-studies. In the first study the items of the GSAAS were generated and field-tested in a cross-sectional non-clinical sample using item analysis, exploratory factor analysis and Rasch analysis.ResultsThis resulted in a 10-item questionnaire measuring a single dimension with good reliability (Cronbach’s α = 0.87). In the second study the 10-item scale was validated using a cross-sectional sample of 496 outpatient adults with mental health problems. Confirmatory factor analysis confirmed the unidimensional structure of the GSAAS and the absence of measurement variance across gender, age and education. Reliability was high (α = 0.89) and moderate to strong correlations between the GSAAS and concurrent validation measures confirmed its convergent validity. Regarding incremental validity, the GSAAS accounted for 7.4% additional explained variance in symptomatic distress above and beyond sense of coherence.DiscussionIn conclusion, the GSAAS appears to be a reliable and valid instrument to assess people’s generic sense of the ability to adapt. It is a practical and quick tool that can be used to measure a vital aspect of health in research and clinical treatment settings

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

ObjectivesAnxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare.MethodsIn the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (< 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data.ResultsML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) < 0.63. Including early change scores slightly improved the models’ performance (AUC range: 0.68–0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58–0.62 vs. 0.58–0.63) and models with early change scores (AUC: 0.69–0.73 vs. 0.68–0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement.ConclusionAccurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress

Introduction of the generic sense of ability to adapt scale and validation in a sample of outpatient adults with mental health problems

Introduction: The ability to adapt is a core aspect of daily human life. Recent models and theories emphasize its essential role for health and well-being. It concerns the perceived ability to readjust and actively deal with the psychosocial consequences of challenging events. While many questionnaires measure competences related to adaptability to specific conditions, a scale that measures a generic sense of the ability to adapt is lacking. The aim of the present study is to introduce the Generic Sense of Ability to Adapt Scale (GSAAS) and to examine its psychometric properties. Methods: The article describes two sub-studies. In the first study the items of the GSAAS were generated and field-tested in a cross-sectional non-clinical sample using item analysis, exploratory factor analysis and Rasch analysis. Results: This resulted in a 10-item questionnaire measuring a single dimension with good reliability (Cronbach’s α = 0.87). In the second study the 10-item scale was validated using a cross-sectional sample of 496 outpatient adults with mental health problems. Confirmatory factor analysis confirmed the unidimensional structure of the GSAAS and the absence of measurement variance across gender, age and education. Reliability was high (α = 0.89) and moderate to strong correlations between the GSAAS and concurrent validation measures confirmed its convergent validity. Regarding incremental validity, the GSAAS accounted for 7.4% additional explained variance in symptomatic distress above and beyond sense of coherence. Discussion: In conclusion, the GSAAS appears to be a reliable and valid instrument to assess people’s generic sense of the ability to adapt. It is a practical and quick tool that can be used to measure a vital aspect of health in research and clinical treatment settings