PurposeTo investigate the perception of dignity among patients hospitalized in a psychiatric setting using the Patient Dignity Inventory (PDI), which had been first validated in oncologic field among terminally ill patients.Patients and methodsAfter having modified two items, we administered the Italian version of PDI to all patients hospitalized in a public psychiatric ward (Service of Psychiatric Diagnosis and Treatment of a northern Italian town), who provided their consent and completed it at discharge, from October 21, 2015 to May 31, 2016. We excluded minors and patients with moderate/severe dementia, with poor knowledge of Italian language, who completed PDI in previous hospitalizations and/or were hospitalized for <72 hours. We collected the demographic and clinical variables of our sample (n=135). We statistically analyzed PDI scores, performing Cronbach’s alpha coefficient and principal factor analysis, followed by orthogonal and oblique rotation. We concomitantly administered to our sample other scales (Hamilton Rating Scales for Depression and Anxiety, Global Assessment of Functioning and Health of the Nation Outcome Scales) to analyze the PDI concurrent validity.ResultsWith a response rate of 93%, we obtained a mean PDI score of 48.27 (±19.59 SD) with excellent internal consistency (Cronbach’s alpha coefficient =0.93). The factorial analysis showed the following three factors with eigenvalue >1 (Kaiser’s criterion), which explained >80% of total variance with good internal consistency: 1) “Loss of self-identity and social role”, 2) “Anxiety and uncertainty for future” and 3) “Loss of personal autonomy”. The PDI and the three-factor scores were statistically significantly positively correlated with the Hamilton Scales for Depression and Anxiety but not with other scale scores.ConclusionOur preliminary research suggests that PDI can be a reliable tool to assess patients’ dignity perception in a psychiatric setting, until now little investigated, helping professionals to improve quality of care and patients to accept treatments.
Background: The recovery model in mental health care emphasizes users’ right to be involved in key decisions of their care, including choice of one’s primary mental health professional (PMHP). Aims: The aim of this article was to provide a scoping review of the literature on the topic of users’ choice, request of change and preferences for the PMHP in community mental health services. Method: A search of the PubMed, Cochrane Library, Web of Science and PsycINFO for papers in English was performed. Additional relevant research articles were identified through the authors’ personal bibliography. Results: A total of 2,774 articles were screened and 38 papers were finally included. Four main aspects emerged: (1) the importance, for users, to be involved in the choice of their PMHP; (2) the importance, for users, of the continuity of care in the relationship with their PMHP; (3) factors of the user/PMHP dyad influencing users’ preferences; and (4) the effect of choice on the treatment outcomes. Conclusion: While it is generally agreed that it is important to consider users’ preferences in choosing or requesting to change their PMHP, little research on this topic is available. PMHPs’ and other stakeholders’ views should also be explored in order to discuss ethical and practical issues.
Sclauzero P., Galli G., Barbati G., Carraro M., Panzetta G.O. (2013). Role of components of frailty on quality of life in dialysis patients: A cross-sectional study. Journal of Renal Care 39(2), 96-102. S U M M A R YBackground: Many people on dialysis suffer a variety of conditions that can affect frailty (the condition or quality of being frail), such as comorbidities, disabilities, dependence, malnutrition, cognitive impairment and poor social conditions. Frailty is suspected to affect quality of life (QoL).Objectives: The study aimed to evaluate the effect of the different components of frailty on the QoL of people on dialysis. Methods: We enrolled 203 out of 233 prevalent patients on dialysis in the Trieste area of Italy. We applied the Short-Form 36 (SF-36) questionnaire, Activities of Daily Living, Instrumental Activities of Daily Living, Subjective Global Assessment scales and Karnofsky Index. In addition we analysed their social conditions. Results: Dependence, malnutrition and disability had a negative role on QoL. Living with family and good social-economic conditions were significantly related to a better QoL. Conclusions: Dependence, malnutrition, disability, poor social and economic conditions have a significant effect on life quality. The role of comorbidities appears to be less important. Screening of patients, nutritional and functional rehabilitation and prevention of social isolation appear to be indispensable in guaranteeing a satisfactory life quality.
Objective: We aimed to predict antipsychotic prescription patterns for people with schizophrenia using machine learning (ML) algorithms. Methods: In a cross-sectional design, a sample of community mental health service users (SUs; n = 368) with a primary diagnosis of schizophrenia was randomly selected. Socio-demographic and clinical features, including the number, total dose, and route of administration of the antipsychotic treatment were recorded. Information about the number and the length of psychiatric hospitalization was retrieved. Ordinary Least Square (OLS) regression and ML algorithms (i.e., random forest [RF], supported vector machine, K-nearest neighborhood, and Naïve Bayes) were used to estimate the predictors of total antipsychotic dosage and prescription of antipsychotic polytherapy (APP). Results: The strongest predictor of the total dose was APP. The number of Community Mental Health Centers (CMHC) contacts was the most important predictor of APP and, with APP omitted, of dosage. Treatment with anticholinergics predicted APP, emphasizing the strong correlation between APP and higher antipsychotic dose. RF performed better than OLS regression and the other ML algorithms in predicting both antipsychotic dose (root square mean error = 0.70, R 2 = 0.31) and APP (area under the receiving operator curve = 0.66, true positive rate = 0.41, and true negative rate = 0.78). Conclusion: APP is associated with the prescription of higher total doses of antipsychotics. Frequent attenders at CMHCs, and SUs recently hospitalized are often treated with APP and higher doses of antipsychotics. Future prospective studies incorporating standardized clinical assessments for both psychopathological severity and treatment efficacy are needed to confirm these findings.
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