Some psychotherapies, such as written exposure therapy for posttraumatic stress disorder, utilize "scripts" during parts of treatment, but verifying script adherence to ensure engagement of key mechanisms of change is a time-consuming step for therapy supervisors. Here, we formalize therapy script adherence as an NLP task, and evaluate several simple (text similarity) and more complex (few-shot LLM) approaches. Over 351 annotated therapist utterance-script pairs, we find text similarity approaches to be highly competitive with LLMs and produce fewer false positives. ROUGE-L recall achieves F1 = 0.973, and BLEU achieves F1 = 0.972 with full precision and zero false positives. GPT-5.2 achieves F1 = 0.935 and GPT-4o-mini achieves F1 = 0.876. Given that the text similarity techniques are multiple orders of magnitude less complex, our results underscore the ability for simpler NLP techniques to still be effective in the age of LLMs for tasks that are more textual in nature, suggesting that aspects of therapist fidelity to evidence-based treatments can be assessed without using cloud API calls.

Psychological states unfold dynamically; to understand and measure mental health at scale we need to detect and measure these changes from sequences of online posts. We evaluate two approaches to capturing psychological changes in text: the first relies on computing the difference between the embedding of a message with the one that precedes it, the second relies on a “human-aware” multi-level recurrent transformer (HaRT). The mood changes of timeline posts of users were annotated into three classes, ‘ordinary,’ ‘switching’ (positive to negative or vice versa) and ‘escalations’ (increasing in intensity). For classifying these mood changes, the difference-between-embeddings technique – applied to RoBERTa embeddings – showed the highest overall F1 score (0.61) across the three different classes on the test set. The technique particularly outperformed the HaRT transformer (and other baselines) in the detection of switches (F1 = .33) and escalations (F1 = .61).Consistent with the literature, the language use patterns associated with mental-health related constructs in prior work (including depression, stress, anger and anxiety) predicted both mood switches and escalations.