Automatic clustering of 1189 Spanish workers identified two distinct mental health profiles—'Female_Burnout' and 'Male_NoBurnout'—with inadequate supervision and lack of reward identified as the most significant predictors of negative mental health outcomes in each cluster, respectively.
Two distinct mental health profiles were identified in the Spanish workforce through automatic clustering analysis.
The 'Female_Burnout' cluster predominantly comprised women aged 31-40 with medium-level job responsibilities who are currently experiencing burnout.
The 'Male_NoBurnout' cluster mainly consisted of men aged 41-55 with low-level job responsibilities who are not currently experiencing burnout.
Inadequate supervision was identified as the most significant predictor of negative mental health outcomes in the 'Female_Burnout' cluster.
Lack of reward was identified as the most significant predictor of negative mental health outcomes in the 'Male_NoBurnout' cluster.
The study found that gender, age, and responsibility level are important factors to consider in the design and implementation of workplace mental health support mechanisms.
This research suggests that workers do not experience mental health challenges in a uniform way—instead, distinct groups of employees face different mental health risks driven by different workplace factors. By analyzing data from nearly 1,200 workers in Spain collected over about a year, the researchers used a statistical technique called automatic clustering to sort participants into two meaningful groups without deciding in advance what those groups would look like. One group was mostly women in their 30s with mid-level job responsibilities who were currently experiencing burnout, while the other was mostly men in their 40s and early 50s in lower-responsibility roles who were not currently burned out but had faced mental health issues in the past. Importantly, the factors most strongly linked to poor mental health differed between these two groups. For the group with more women experiencing burnout, poor-quality supervision from managers was the strongest predictor of mental health problems. For the predominantly male group without current burnout, feeling insufficiently rewarded for their work was the key factor. Both groups had histories of work-related mental health difficulties, suggesting that past mental health challenges are a shared vulnerability across different worker profiles. This research suggests that one-size-fits-all approaches to workplace mental health may miss important differences between employee groups. Organizations looking to support employee well-being might benefit from tailoring their interventions—for example, focusing on improving management and supervisory practices for certain groups, while addressing compensation and recognition systems for others. The findings also highlight that demographic characteristics like gender, age, and level of job responsibility should be taken into account when designing workplace mental health programs.
Nia Plamenova Djourova, Ana Freire, A. Theben. (2026). Decoding workplace mental health: Automatic clustering of the Spanish workforce.. Acta Psychologica. https://doi.org/10.1016/j.actpsy.2026.106730