Explaining GPTs’ schema of depression: A machine behavior analysis.
Adithya V Ganesan; Vasudha Varadarajan; Yash Kumar Lal; Veerle Celine Eijsbroek; Katarina Kjell; Oscar Kjell; Tanuja Dhanasekaran; Elizabeth Cameron Stade · 2026 · Journal of Psychopathology and Clinical Science
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
. GPT-5 showed slightly lower convergence with self-report, which our analysis attributed to changes in the relationships among symptoms. These insights provide a generalizable, empirical foundation for understanding language models'…
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Our Princess Is in Another Castle
Negative / Null Result ReportUsing Psychological Artificial Intelligence (Tess) to Relieve Symptoms of Depression and Anxiety: Randomized Controlled Trial
Negative / Null Result ReportHow Replicable Are Links Between Personality Traits and Consequential Life Outcomes? The Life Outcomes of Personality Replication Project
Negative / Null Result ReportThe cross-cultural validity of posttraumatic stress disorder: implications for DSM-5
Negative / Null Result ReportPsychiatric and medical correlates of DSM‐5 eating disorders in a nationally representative sample of adults in the United States
Negative / Null Result ReportRomantic relationships and the physical and mental health of college students
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1037/abn0001144