The main purpose for the expansion of supported community care for persons with serious mental illness in Sweden was to ensure the right for these persons to live as citizens in the community. However, earlier research shows that negative attitudes towards mental illness present an obstacle for social integration of persons with serious mental illness. The aim of this study, conducted in Sweden, was to evaluate an existing instrument's (Community Attitudes towards Mental Illness, CAMI), validity and reliability. An additional aim was to adapt and develop the questionnaire to Swedish circumstances. After translation and modification of the original CAMI, the Swedish version of the questionnaire (CAMI-S) was distributed to all student nurses at three different universities in Sweden. The overall Cronbach's alpha coefficient was 0.90 of the original CAMI-S. A corrected inter-item total correlation excluded 20 items because they showed loading <0.43. The overall Cronbach's alpha coefficient on the 20 items (new CAMI-S) that showed loading, >0.43, was 0.903. A factor analysis of these items revealed that the data could be extracted in three factors labelled as: open-minded and pro-integration, fear and avoidance and community mental health ideology. Finally, in order to reach reliable results in attitude research, it is important to measure the respondent's attitude towards the object in common as well as the respondent's attitude to interact with the object. Accordingly, it is important to add behavioural intention items to the 'new CAMI-S'. Statements exemplifying how something 'ought to be' in an impersonal way have a good degree of stability over time and place.
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http://dx.doi.org/10.1111/j.1447-0349.2008.00552.x | DOI Listing |
Foot Ankle Surg
January 2025
Foot and Ankle Division, Department of Orthopaedic Surgery, NYU Langone Health, New York City, NY 10002, USA. Electronic address:
Background: The purpose of this systematic review was to evaluate the impact of mental health disorders (MHDs) on the clinical and functional outcomes following total ankle arthroplasty (TAA) for the treatment of end-stage ankle arthritis.
Methods: A systematic review of the EMBASE, MEDLINE, and Cochrane Library databases was conducted in April 2024 following PRISMA guidelines. Data collected included patient demographics, clinical outcomes, complications, and failures.
Neurosci Biobehav Rev
January 2025
Department of Psychology, Sapienza University of Rome, Rome, Italy; Body and Action Lab, IRCCS Fondazione Santa Lucia, Rome, Italy. Electronic address:
Introduction: Brain and sleep development in childhood shapes emotional and cognitive growth, including the ability to recall dreams. In line with the continuity hypothesis of dreaming, several findings suggest a link between clinical symptoms and nightmare frequency. Sleep disorders and anxiety are among the most frequently co-occurring conditions in children and adolescents with autism spectrum disorder (ASD).
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Neurology, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Anhui Province, Hefei 230022, China; Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China; Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China. Electronic address:
Background: Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but the underlying neuromodulatory mechanisms remain largely unknown. Functional stability represents a newly developed method based on the dynamic functional connectivity framework. This study aimed to explore ECT-evoked changes in functional stability and their relationship with clinical outcomes.
View Article and Find Full Text PDFJ Affect Disord
January 2025
Department of Psychiatry and Psychotherapy, University of Marburg, Germany; Center for Mind, Brain and Behavior (CMBB), University of Marburg, Germany.
Background: Major depressive disorder (MDD) comes along with an increased risk of recurrence and poor course of illness. Machine learning has recently shown promise in the prediction of mental illness, yet models aiming to predict MDD course are still rare and do not quantify the predictive value of established MDD recurrence risk factors.
Methods: We analyzed N = 571 MDD patients from the Marburg-Münster Affective Disorder Cohort Study (MACS).
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