The International Mental Health Assessment: Validation of an Efficient Screening Inventory

Abstract

The International Mental Health Assessment (IMHA) was developed to provide efficient screening to facilitate prevention and early intervention among employees or community adults at three levels of analysis: a P-factor of general functioning and tendency toward disorder; broad spectra of internalizing and externalizing tendencies and for life difficulties; and nine subscales for common, familiar psychological and behavioral health categories. This study describes the development, refinement, and validation of the inventory using item response theory (IRT), specifically the partial credit model (PCM). Explicit, behavior-focused items drew on commonalities among domain-specific inventories, the DSM-V and empirical literature. A response scale based on concrete frequency of occurrence over the last month was developed to avoid the reference-group effects that plague cross-group survey research, facilitating cross-group comparison at both scale and item levels. In Study 1, a preliminary 69-item version was administered to 5,307 employees, family members, and counseling clients. PCM calibration was used to remove items with overlapping discrimination or unclear scale correspondence. In Study 2, the refined 59-item IMHA was administered to 4,048 employees. In Study 3, the subscales were compared to relevant established inventories to assess and confirm their convergent/divergent validity in a third sample (N = 500). The final 54-item IMHA, intended both for screening for psychological problems among community adults and to facilitate research including cross-cultural and cross-group comparisons, is made available freely for educational, non-profit or research purposes. The three-level measurement strategy draws on recent evidence for the continuous nature of psychopathology and on the well-established co-morbidity of traditional disorder categories, making use of them for communication purposes without unnecessarily reifying them in the model.