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1.
Int J Law Psychiatry ; 54: 36-45, 2017.
Article in English | MEDLINE | ID: mdl-28962685

ABSTRACT

Instruments designed to evaluate the necessity of compulsory psychiatric treatment (CPT) are scarce to non-existent. We developed a 25-item Checklist (scoring 0 to 50) with four clusters (Legal, Danger, Historic and Cognitive), based on variables identified as relevant to compulsory treatment. The Compulsory Treatment Checklist (CTC) was filled with information on case (n=324) and control (n=251) subjects, evaluated under the Portuguese Mental Health Act (Law 36/98), in three hospitals. For internal validation, we used Confirmatory Factor Analysis (CFA), testing unidimensional and bifactor models. Multilevel logistic regression model (MLL) was used to predict the odds ratio (OR) for compulsory treatment based on the total scale score. Receiver Operating Characteristic analysis (ROC) was performed to predict compulsory treatment. CFA revealed the best fit indexes for the bifactor model, with all items loading on one General factor and the residual loading in the a priori predicted four specific factors. Reliability indexes were high for the General factor (88.4%), and low for specific factors (<5%), which demonstrate that CTC should not be performed in the subscales to access compulsory treatment. MLL reveals that for each item scored in the scale, it increases the OR by 1.26 for compulsory treatment (95%CI 1.21-1.31, p<0.001). Based on the total score, accuracy was 90%, and the best cut-off point of 23.5 detects compulsory treatment with a sensitivity of 75% and specificity of 93.6%. The CTC presents robust internal structure with a strong unidimensional characteristic, and a cut-off point for compulsory treatment of 23.5. The improved 20-item version of the CTC could represent an important instrument to improve clinical decision regarding CPT, and ultimately to improve mental health care of patients with severe psychiatric disorders.


Subject(s)
Checklist/instrumentation , Checklist/standards , Commitment of Mentally Ill , Forensic Psychiatry/instrumentation , Mental Disorders/diagnosis , Psychiatric Status Rating Scales , Adolescent , Adult , Aged , Aged, 80 and over , Case-Control Studies , Cognition , Commitment of Mentally Ill/legislation & jurisprudence , Factor Analysis, Statistical , Female , Hospitals , Humans , Logistic Models , Male , Mandatory Programs , Mental Disorders/therapy , Mental Health/legislation & jurisprudence , Middle Aged , Portugal , Psychiatric Status Rating Scales/standards , Psychometrics , Reproducibility of Results , Sensitivity and Specificity , Violence , Young Adult
2.
Psychiatry Res ; 217(3): 210-4, 2014 Jul 30.
Article in English | MEDLINE | ID: mdl-24745473

ABSTRACT

Lifelong learning (LLL) is an essential feature for the doctor to keep clinically updated and has been described as an indicator of competence and professionalism. The aim of this study was to evaluate the association between career satisfaction in psychiatry, lifelong learning, and commitment in scientific activities, taking into account other personal and professional effects. The survey was sent to 453 national psychiatrists and 190 surveys (41.9%) were completed online and validated. The Jefferson Scale of Physician Lifelong Learning (JSPLL) was used to assess the level of LLL for each doctor. The results of the analysis of JSPLL showed that participants more satisfied with their career have greater motivation and invest more in the LLL. Furthermore, participants who were more satisfied with their career had a higher percentage of scientific activity in the last year. Multiple linear regression with these two effects in the model revealed a positive association between career satisfaction in psychiatry, LLL and the publication of scientific papers, leading to the main conclusion that satisfaction with a career in psychiatry has a significant correlation with LLL and with involvement in scientific activities.


Subject(s)
Job Satisfaction , Learning , Personal Satisfaction , Physicians/psychology , Psychiatry , Adult , Aged , Data Collection , Female , Humans , Male , Middle Aged , Motivation , Surveys and Questionnaires , Workforce
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