Motivational Interviewing

Practise Motivational Interviewing with a patient who is deeply ambivalent about change. Explore her world with OARS — Open Questions, Affirmations, Reflections, and Summaries.

What you practise

• Practise using Open Questions, Affirmations, Reflections, and Summaries (OARS)
• Develop skill in evoking the patient's own motivation for change rather than providing it
• Learn to recognise and respond to change talk (DARN) and sustain talk
• Experience resisting the righting reflex — the urge to fix, advise, or argue
• Understand the Spirit of MI: partnership, acceptance, compassion, and evocation
• Practise advanced MI techniques: decisional balance, importance/confidence rulers, reframing

Who you can meet

• Andrea, 56 — 56-year-old long-time smoker, scared after coughing blood. Deeply ambivalent about quitting — everyone around her smokes. Her pregnant daughter is her strongest motivation.
• Felicia, 27 — 27-year-old bartender with escalating alcohol use, severe anxiety, and a social life built entirely around drinking. She sees the damage but cannot imagine who she is without it.
• Nora, 29 — 29-year-old UX designer who uses cannabis five to six evenings per week to fall asleep. She does not see herself as an addict and does not want abstinence imposed on her. Rational, guarded, and genuinely ambivalent — her reasons to use are as real as her reasons to stop.
• Mikael, 42 — 42-year-old warehouse supervisor with obesity, loss-of-control evening eating, and compulsive training despite pain and exhaustion. He wants better health for his family but fears that easing up means giving in.

What the feedback looks at

• Cultivating Change Talk
• Softening Sustain Talk
• Partnership
• Empathy

Rated against: Adapted from Moyers, T.B., Manuel, J.K. & Ernst, D. (2014, rev. 2015), Motivational Interviewing Treatment Integrity Coding Manual 4.2.1, University of New Mexico (CASAA). Anchor wording is PsychBase's own adaptation. These scores are generated by AI and are not equivalent to coding by a trained MITI coder.

This lab is for clinical training. The patient is generated by an AI language model from a written case; no real patient and no patient data are involved. Browsing is open; starting a session needs a free account.