Amid the ongoing mental health crisis, there is an increasing need to discern possible signs of mental disturbance manifested in social media text. During in-person therapy sessions, mental health professionals employ manual methods to identify the root causes and outcomes of latent factors contributing to mental disturbances, which is a painstaking and time-intensive process. Neglecting multi-dimensional aspects of well-being (i.e., wellness dimensions) over time can adversely affect an individual’s mind. To enable such fine-grained mental health screening, we define the task of identifying wellness concepts and classifying in pre-defined dimensions in Reddit posts. We construct a novel dataset called WellXplain, which consists of 3,092 instances and a total of 72,813 words. Our experts developed an annotation scheme based on a well-adapted Halbert L. Dunn’s theory of wellness dimensions. Further, the data encompasses human-annotated text spans as pertinent explanations for decision-making during wellness concept classification. We anticipate that releasing the dataset and evaluating the baselines will facilitate the development of new language models for concept extraction and classification in healthcare domain.