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Background: Peer support is an emotional, social, and practical help provided by nonprofessionals to assist others in sustaining health behaviors. Peer support is valued in recovery-oriented models of mental health and is becoming increasingly implemented at the organizational level. Text messaging is a relatively low-cost, high-impact, and easily scalable program that uses existing technology, is devoid of geographic barriers, and is easily accessible to end users. Objective: This study aims to evaluate the effectiveness of an innovative peer support system plus a supportive text messaging program on the recovery of discharged patients from acute psychiatric care. Methods: This prospective, rater blinded, controlled observational study included 181 patients who were discharged from acute psychiatric care. Patients were randomized to one of four conditions: treatment as usual (follow-up care), daily supportive text messages only, peer support only, or peer support plus daily supportive text messages. A standardized self-report measure of recovery (Recovery Assessment Scale [RAS]) was completed at baseline, 6 weeks, 3 months, and 6 months. Descriptive analysis, one-way analysis of variance, and repeated measures multivariate analysis of covariance were used to examine the changes in the RAS among the study groups and over the follow-up time points. Results: A total of 65 patients completed the assessments at each time point. For the overall sample, higher scores were found for the peer support plus text message condition compared with the text message only and treatment as usual condition on several scales (ie, willingness to ask for help and personal confidence and hope) and total score on the RAS, after 6 months of intervention. Conclusions: Peer support plus supportive text messaging seems to result in improved recovery compared with other interventions. It may be advisable to incorporate the two interventions as part of routine practice for patients with psychiatric disorders upon hospital discharge. [ABSTRACT FROM AUTHOR]

Objective: To use clinically informed machine learning to derive prediction models for early and late premature death in epilepsy. Methods: This was a population‐based primary care observational cohort study. All patients meeting a case definition for incident epilepsy in the Health Improvement Network database for inclusive years 2000‐2012 were included. A modified Delphi process identified 30 potential risk factors. Outcome was early (within 4 years of epilepsy diagnosis) and late (4 years or more from diagnosis) mortality. We used regularized logistic regression, support vector machines, Gaussian naive Bayes, and random forest classifiers to predict outcomes. We assessed model calibration, discrimination, and generalizability using the Brier score, mean area under the receiver operating characteristic curve (AUC) derived from stratified fivefold cross‐validation, plotted calibration curves, and extracted measures of association where possible. Results: We identified 10 499 presumed incident cases from 11 194 182 patients. All models performed comparably well following stratified fivefold cross‐validation, with AUCs ranging from 0.73 to 0.81 and from 0.71 to 0.79 for early and late death, respectively. In addition to comorbid disease, social habits (alcoholism odds ratio [OR] for early death = 1.54, 95% confidence interval [CI] = 1.12‐2.11 and OR for late death = 2.62, 95% CI = 1.66‐4.16) and treatment patterns (OR for early death when no antiseizure medication [ASM] was prescribed at baseline = 1.33, 95% CI = 1.07‐1.64 and OR for late death after receipt of enzyme‐inducing ASM at baseline = 1.32, 95% CI = 1.04‐1.66) were significantly associated with increased risk of premature death. Baseline ASM polytherapy (OR = 0.55, 95% CI = 0.36‐0.85) was associated with reduced risk of early death. Significance: Clinically informed models using routine electronic medical records can be used to predict early and late mortality in epilepsy, with moderate to high accuracy and evidence of generalizability. Medical, social, and treatment‐related risk factors, such as delayed ASM prescription and baseline prescription of enzyme‐inducing ASMs, were important predictors. [ABSTRACT FROM AUTHOR]

Background. Currently, Ghana has 14 actively practicing psychiatrists and about 26 psychiatric residents for a population of over 28 million people. Previous research suggests a lack of interest by Ghanaian medical students and medical graduates in considering psychiatry as a career option. Objectives. To examine the perception of medical students and psychiatry residents in Ghana about the barriers which hinder Ghanaian medical graduates from choosing careers in psychiatry and how these barriers could be overcome. Methods. This was a cross-sectional qualitative study with data gathered using focus group discussion. Twenty clinical year medical students were selected through block randomization from the four public medical schools in Ghana and invited to participate in one of two focus group discussions. Also, four psychiatric residents were invited to participate in the focus group discussions. Results. The main barriers identified by participants could be grouped under four main themes, namely: (a) myths and stigma surrounding mental health and patients, (b) negative perceptions of psychiatrists, (c) infrastructure and funding issues, (d) lack of exposure and education. To address the barriers presented, participants discussed potential solutions that could be categorized into five main themes, namely: (a) stigma reduction, (b) educating professionals, (c) addressing deficient infrastructure, (d) risk management, and (e) incentivizing the pursuit of psychiatry among students. Conclusion. Health policy planners and medical training institutions could consider implementing proposed solutions to identify barriers as part of efforts to improve the psychiatrist to patient ratio in Ghana. [ABSTRACT FROM AUTHOR]

The article discusses the results of a study on the impact of COVID-19 pandemic on the prevalence of symptoms of anxiety in various individuals in Canada. Topics discussed include the use of supportive text messages for cognitive behavioral therapy, the classification of individuals with moderate to high anxiety symptoms, and the protective social factors that benefited most of the respondents.

Objective: Little is known about the validity of computerized cognitive batteries, such as CNS Vital Signs (CNSVS), in pediatric patients. The purpose of this study was to examine convergent and divergent validity of the CNSVS in a clinical pediatric sample with neurological diagnoses. Method: Participants included 123 pediatric patients assessed in a tertiary care setting as part of clinical care. CNSVS (Memory, Psychomotor Speed, Reaction Time, Complex Attention, and Cognitive Flexibility domains, and a Neurocognition Index) and paper-and-pencil neuropsychological measures assessing learning, memory, processing speed, reaction time, attention, and executive functioning were administered Results: Most correlations between CNSVS domain scores and neuropsychological measures assessing similar constructs were medium in strength. With the exception of stronger correlations between psychomotor speed tests, correlations between tests of similar constructs were not significantly higher than those between dissimilar constructs. Conclusions: These results provide support for validity of the CNSVS battery, but also caution that many abilities are inter-correlated. [ABSTRACT FROM AUTHOR]

Purpose:The purpose of this article was to investigate the accuracy of the WISC-IV short forms in estimating Full Scale Intelligence Quotient (FSIQ) and General Ability Index (GAI) in pediatric epilepsy. Methods:One hundred and four children with epilepsy completed the WISC-IV as part of a neuropsychological assessment at a tertiary-level children’s hospital. The clinical accuracy of eight short forms was assessed in two ways: (a) accuracy within +/− 5 index points of FSIQ and (b) the clinical classification rate according to Wechsler conventions. The sample was further subdivided into low FSIQ (≤ 80) and high FSIQ (> 80). Results:All short forms were significantly correlated with FSIQ. Seven-subtest (Crawford et al. [2010] FSIQ) and 5-subtest (BdSiCdVcLn) short forms yielded the highest clinical accuracy rates (77%–89%). Overall, a 2-subtest (VcMr) short form yielded the lowest clinical classification rates for FSIQ (35%–63%). The short form yielding the most accurate estimate of GAI was VcSiMrBd (73%–84%). Conclusions:Short forms show promise as useful estimates. The 7-subtest (Crawford et al., 2010) and 5-subtest (BdSiVcLnCd) short forms yielded the most accurate estimates of FSIQ. VcSiMrBd yielded the most accurate estimate of GAI. Clinical recommendations are provided for use of short forms in pediatric epilepsy. [ABSTRACT FROM AUTHOR]