A Proposed Taxonomy to Holistically Classify Employee Mental Health Programs: Qualitative Taxonomy Development Study

The number and diversity of employee mental health programs (EMHPs), solutions employers offer to their workforce to improve mental health, have expanded rapidly in recent years, driven by advancements in digital technology and increased global awareness of employee mental health. This dynamic has resulted in a diverse and nontransparent EMHP landscape. While existing taxonomies address specific aspects of mental health programs, a comprehensive taxonomy for classifying EMHPs in more detail remains absent. Establishing such a taxonomy would benefit researchers and practitioners by providing a common standard for categorizing EMHPs and thereby enhance transparency.

This research aimed to develop and evaluate a comprehensive taxonomy to holistically classify EMHPs, providing a practical and standardized tool for various target groups to categorize, develop, and select EMHPs.

A thorough taxonomy development process with 4 iterations was applied. The first 2 iterations used conceptual-to-empirical approaches and involved scoping reviews to identify relevant dimensions and characteristics of EMHPs. The latter 2 iterations used empirical-to-conceptual approaches and included semistructured qualitative interviews. The third iteration, involving employee interviews, aimed to identify further dimensions and characteristics of EMHPs to develop the initial taxonomy. During the fourth iteration, 17 international experts were interviewed to refine and validate the initial taxonomy. After the fourth iteration, the taxonomy was evaluated by applying it to 3 real-world EMHPs through a focus group with 5 experts to ensure that all ending conditions and the evaluation goals were met. The interrater reliability was analyzed using the proportion of observed agreement and Fleiss κ.

The resulting taxonomy comprises 2 metadimensions, 21 dimensions, and 69 characteristics, offering a standardized framework for EMHP classification and analysis. Experts successfully applied the taxonomy to classify 3 selected EMHPs, resulting in an overall proportion of observed agreement of 85% and a Fleiss κ of 66%. Across dimensions, the proportion of observed agreement ranged from 64% to 100%, with Fleiss κ ranging from 20% to 100% (P values ranging from P=.004 to P<.001).

This taxonomy contributes to establishing a common standard for holistic EMHP classification. It benefits both mental health researchers and practitioners in fostering transparency and serves as a structured tool for EMHP analysis. The taxonomy enables researchers to conduct relevant future research, including the systematic identification of EMHP archetypes. In practice, the taxonomy can guide providers in market gap identification and EMHP development, inform employers in decision-making, and assist policymakers in setting up targeted support mechanisms for EMHP implementation.