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Revisiting the Semantic Severity of Anxiety and Depression: Computational Linguistic Study of Normalization and Pathologization

Revisiting the Semantic Severity of Anxiety and Depression: Computational Linguistic Study of Normalization and Pathologization

First, we trained a Continuous Bag of Words word2vec model on the entire corpus using the word2vec package in R (R Foundation for Statistical Computing) [27]. Next, we created a mental health vector by averaging the vectors of words strongly associated with mental health (eg, therapy, psychiatry, and diagnosis; see Multimedia Appendix 1 for details). This vector was used as a reference point for identifying mental health–related language.

Vojtech Pisl, Ana-Maria Bucur, Ioana R Podina

J Med Internet Res 2025;27:e73950

Development of a Cocreated Decision Aid for Patients With Depression—Combining Data-Driven Prediction With Patients’ and Clinicians’ Needs and Perspectives: Mixed Methods Study

Development of a Cocreated Decision Aid for Patients With Depression—Combining Data-Driven Prediction With Patients’ and Clinicians’ Needs and Perspectives: Mixed Methods Study

Statistical analyses were performed using RStudio IDE (version 1.4.1103) running R (version 4.0.3). Sensitivity analyses were performed to investigate whether different patient selection criteria would result in larger sample sizes and different distributions of treatment data.

Kaying Kan, Frederike Jörg, Klaas J Wardenaar, Frank J Blaauw, Maarten F Brilman, Ellen Visser, Dennis Raven, Dwayne Meijnckens, Erik Buskens, Danielle C Cath, Bennard Doornbos, Robert A Schoevers, Talitha L Feenstra

J Particip Med 2025;17:e67170