Leveraging Multimodal Self-Consistency Reasoning in Coding Motivational Interviewing for Alcohol Use Reduction

In three linesarXiv study on automating Motivational Interviewing (MI) session coding for alcohol use reduction. Uses audio-language models (ALMs) with 4 complementary analytic prompts and self-consistency (12 reasoning trajectories per utterance). On 5 sessions, achieves 52.56% accuracy, 54.03% precision, 47.45% recall, macro-F1 46.40%, exceeding baselines.

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