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Exploration of Psychiatry Residents' Attitudes toward Patients with Substance Use Disorder, Bipolar Disorder and Schizophrenia in Saudi Arabia.

Abdullah M AlarifiNajim Z AlshahraniNawaf H AlbaliKhalid M AljalajelNourh M AlotaibiAnan A FallatahMajd Rachid ZeitounieKhalid A AlghamdiMaan A AlsaaidAhmed Alshehri
Published in: Behavioral sciences (Basel, Switzerland) (2023)
Stigmatizing attitudes of psychiatry professionals toward patients with various mental disorders may negatively impact treatment-seeking behaviors. However, in Saudi Arabia, little is known about psychiatry residents' attitudes toward individuals with a specific disease/disorder. Therefore, the purpose of this study was to assess psychiatry residents' attitudes toward patients with substance use disorder (SUD), bipolar disorder and schizophrenia in Saudi Arabia. Data for this cross-sectional study were collected from psychiatry residents (N = 79) in Saudi Arabia with a structured questionnaire containing sociodemographic and attitude-related variables. The 11-item Medical Condition Regard Scale (MCRS) for individuals with three conditions was used to assess participants' attitudes. A linear regression model was fitted to investigate the association. Based on the MCRS (on a scale of 11 to 66), participants' mean attitude scores were 41.59 (SD: 8.09), 54.53 (SD: 5.90) and 54.20 (SD: 6.60) for SUD, bipolar disorder and schizophrenia, respectively. Adjusted regression analysis demonstrated that senior residents, an age ≥ 27 years and a high confidence level were significantly associated with psychiatry residents' positive attitudes toward patients with the three conditions. Psychiatry residents' attitude scores were relatively lower (i.e., negative attitudes) for patients with SUD than for those with bipolar disorder and schizophrenia. Future longitudinal studies are recommended to explore the factors behind psychiatry residents' negative attitudes toward patients with addictive behaviors and mental illnesses.
Keyphrases
  • bipolar disorder
  • major depressive disorder
  • mental health
  • healthcare
  • deep learning
  • neural network