Clinical medical practice and stigma towards patients with substance use disorder in an Italian sample of healthcare workers.

INTRODUCTION: People with substance use disorder (SUD) face challenges like stigma and discrimination, impacting their healthcare experiences. AIM: This study aims to: (i) assess physicians' clinical practices and stigma toward SUD patients among healthcare personnel and (ii) explore the relationship among stigma, psychological well-being, and burnout. METHODS: A survey covering sociodemographic data, physicians' clinical practices, stigmatizing attitudes, psychological well-being, and burnout was completed by 1,796 employees of the Veneto's Local Health Units (Italy). RESULTS: Healthcare professionals reported increased stigma towards SUDs (p-values<0.05). Stigma consistently correlated with variables such as sex, profession, department, and levels of burnout (p-values<0.05). Notably, high burnout levels were associated with increased stigma. Staff in addiction departments displayed lower stigma levels compared to other departments. No significant differences were found in physicians' clinical practices. CONCLUSIONS: Targeted training for healthcare professionals is crucial to reduce stigma, enhance attitudes toward SUDs, and broaden overall knowledge of the condition.

Centered Partition Processes: Informative Priors for Clustering (with Discussion).

There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations in terms of Exchangeable Partition Probability Functions (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and Pitman-Yor processes. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concrete prior knowledge on the partition. For example, we are motivated by an epidemiological application, in which we wish to cluster birth defects into groups and we have prior knowledge of an initial clustering provided by experts. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process that modifies the EPPF to favor partitions close to an initial one. Some properties of the CP prior are described, a general algorithm for posterior computation is developed, and we illustrate the methodology through simulation examples and an application to the motivating epidemiology study of birth defects.