Title: Interpretable (meta)factorization of clinical questionnaires to identify general dimensions of psychopathology. Abstract: Psychiatry research aims at understanding manifestations of psychopathology in behavior, in terms of a small number of latent constructs. These are usually inferred from questionnaire data using factor analysis. The resulting factors and relationship to the original questions are not necessarily interpretable. Furthermore, this approach does not provide a way to separate the effect of confounds from those of constructs, and requires explicit imputation for missing data. Finally, there is no clear way to integrate multiple sets of constructs estimated from different questionnaires. An important question is whether there is a universal, compact set of constructs that would span all the psychopathology issues listed across those questionnaires.  We propose a new matrix factorization method designed for questionnaires aimed at promoting interpretability, through bound and sparsity constraints. We provide an optimization procedure with theoretical convergence guarantees, and validate automated methods to detect latent dimensionality on synthetic data. We first demonstrate the method on a commonly used general-purpose questionnaire. We then show it can be used to extract a broad set of 15 psychopathology factors spanning 21 questionnaires from the Healthy Brain Network study. We show that our method preserves diagnostic information against competing methods, even as it imposes more constraints. Finally, we demonstrate that it can be used for defining a short, general questionnaire that allows recovery of those 15 meta-factors, using data more efficiently than other methods.