  Self-guided mental health interventions, such as "do-it-yourself" tools to
learn and practice coping strategies, show great promise to improve access to
mental health care. However, these interventions are often cognitively
demanding and emotionally triggering, creating accessibility barriers that
limit their wide-scale implementation and adoption. In this paper, we study how
human-language model interaction can support self-guided mental health
interventions. We take cognitive restructuring, an evidence-based therapeutic
technique to overcome negative thinking, as a case study. In an IRB-approved
randomized field study on a large mental health website with 15,531
participants, we design and evaluate a system that uses language models to
support people through various steps of cognitive restructuring. Our findings
reveal that our system positively impacts emotional intensity for 67% of
participants and helps 65% overcome negative thoughts. Although adolescents
report relatively worse outcomes, we find that tailored interventions that
simplify language model generations improve overall effectiveness and equity.
