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Background: Providing efficient and targeted services for patients with mental health problems requires efficient collaboration and coordination within healthcare providers, but measuring collaboration using traditional methods is challenging. Aims: To explore the patient-sharing networks of professionals taking care of different groups of patients with mental or substance use disorders. Method: We used data that covered adult patients' visits to the primary care service providers of seven municipalities in Finland during year 2021. Data included 8,217 patients (147,430 visits) with mental or substance use disorders who were treated by 1,566 health care professionals. We calculated descriptive network metrics to examine the connectivity of professionals in three different patient groups (patients with substance use disorders, psychotic disorders, and depressive disorders) and compared these characteristics to a network based on all patients. We also analyzed whether patient sharing was associated with the health care professionals' attributes (occupational group, municipality) using Exponential Random Graph Models (ERGM). Results: Diagnosis-specific networks were denser and more connected compared to the all-patients network. Nurses were the most central occupation in all the diagnosis-specific networks and especially in the substance use disorder patients network. When examining all patients, two professionals were more likely to share patients when they belonged to the same occupational group. However, in the network with depressive disorder patients we found the opposite: professionals were more likely to share patients if they were of different occupational groups. Conclusions: Patient-sharing networks within patients with a specific mental or substance use disorders are denser and more connected than networks based on all patients with mental or substance use disorders. In the substance use disorder patients network particularly, nurses were the most central occupation. Multi-professional connections were more likely in depressive disorder networks than in the all-patients network.

PurposeA prominent labour market feature in recent decades has been the increase in abstract and service jobs, while the demand for routine work has declined. This article examines whether the components of Type A behaviour predict workers' selection into non-routine abstract, non-routine service and routine jobs.Design/methodology/approachBuilding on the work by Barrick et al. (2013), this article first presents how the theory of purposeful work behaviour can be used to explain how individuals with different levels of Type A components sort into abstract, service and routine jobs. Then, using longitudinal data, it examines whether the components of Type A behaviour predict occupational sorting. Estimations were performed based on the linear regression method.FindingsThe results show that the Type A dimension "leadership" was associated with a higher level of abstract and service job tasks in occupation. High eagerness-energy and responsibility were also positively linked with occupation's level of abstract tasks. These results suggest that workers sort into jobs that allow them to pursue higher-order implicit goals.Originality/valueJob market polarisation towards low-routine jobs has had a pervasive influence on the labour market during the past few decades. Based on high-quality data that combine prime working-age register information on occupational attainment with information about personality characteristics, the findings contribute to our knowledge of how personality characteristics contribute to occupational sorting in terms of this important job aspect.