How do you feel? Using natural language processing to automatically rate emotion in psychotherapy.

Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, systematic research examining patterns of emotional exchange that occur during psychotherapy sessions is often limited in scale. Traditional methods for identifying emotion in psychotherapy rely on labor-intensive observer ratings, client or therapist ratings obtained before or after sessions, or involve manually extracting ratings of emotion from session transcripts using dictionaries of positive and negative words that do not take the context of a sentence into account.

"It's hard to argue with a computer:" Investigating Psychotherapists' Attitudes towards Automated Evaluation.

We present CORE-MI, an automated evaluation and assessment system that provides feedback to mental health counselors on the quality of their care. CORE-MI is the first system of its kind for psychotherapy, and an early example of applied machine-learning in a human service context. In this paper, we describe the CORE-MI system and report on a qualitative evaluation with 21 counselors and trainees.

Variability in motivational interviewing adherence across sessions, providers, sites, and research contexts.

Providers' adherence in the delivery of behavioral interventions for substance use disorders is not fixed, but instead can vary across sessions, providers, and intervention sites. This variability can substantially impact the quality of intervention that clients receive. However, there has been limited work to systematically evaluate the extent to which substance use intervention adherence varies from session-to-session, provider-to-provider, and site-to-site.

A multivariate meta-analysis of motivational interviewing process and outcome.

Motivational interviewing (MI) theory proposes a process whereby a set of therapist behaviors has direct effects on client outcomes and indirect effects through in-session processes (e.g., client change talk).

Chronic Disease Risks From Exposure to Long-Hour Work Schedules Over a 32-Year Period.

Objectives: This study aims at evaluating the chronic disease risk related to prolonged work in long-hour schedules for eight major chronic diseases: heart disease, non-skin cancer, arthritis, diabetes, chronic lung disease, asthma, chronic depression, and hypertension.

Methods: The study used data from the National Longitudinal Survey of Youth, 1979 covering 32 years of job history (1978 to 2009) for 7492 respondents. Logistic regression analyses were performed to test the relationship between average weekly work hours, and the reported prevalence of those conditions for each individual.