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

Objectives: Anxiety 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.

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.

Validation of the Mental Health Continuum-Short Form and the dual continua model of well-being and psychopathology in an adult mental health setting.

Objective: The growing evidence for the dual continua model of psychopathology and well-being has important implications for measuring outcomes in mental health care. The aim of the current study is to validate a measure of well-being as well as the dual continua model in adults with mood, anxiety, personality, and developmental disorders.

Methods: 472 adult psychiatric outpatients filled out the Mental Health Continuum-Short Form (MHC-SF) and the Outcome Questionnaire before start of treatment.