Early maladaptive schema, attachment style, and parenting style in a clinical population with personality disorder and normal individuals: a discriminant analysis model.

INTRODUCTION: Researchers have shown various variables' role in forming personality disorders (PD). This study aimed to assess the role of early maladaptive schema (EMS), attachment style (AS), and parenting style (PS) in discriminating between personality disorders and normal individuals. METHODS: In this study, 78 personality disorder patients and 360 healthy volunteers aged 18-84 were selected using convenience sampling. They completed the Schema Questionnaire-Short Form (SQ-SF), Revised Adult Attachment Scale (RAAS), and Baumrind's Parenting Styles Questionnaire (PSI). Data were analyzed using discriminant analysis with IBM SPSS 25. RESULTS: The results showed higher mean scores in all early maladaptive schema domains, insecure attachment styles, and authoritarian parenting in the personality disorder group than in the normal group. Also, discriminant analyses revealed that the function was statistically significant and could distinguish between the two groups and a compound of essential variables, disconnection, impaired autonomy, and secure attachment, respectively, discriminating two groups. Given that all components were able to distinguish between the two groups. CONCLUSION: Therefore, intervention based on these factors early in life may help reduce the characteristics of personality disorders. Also, considering the role of these factors, treatment protocols can be prepared.

Psychometric Properties of the Persian Version of Social Anxiety Scale for Social Media Users (SAS-SMU).

Objective: Social anxiety refers to an excessive concern or fear about social situations. It seems that social media, which has become one of the most popular and effective tools for communication today, can be one of the contexts of social anxiety. Due to the lack of a Persian instrument to assess social anxiety in the context of social networks, the current study was undertaken to analyze the psychometric criteria of the Iranian version of the Social Anxiety Scale for Social Media Users (SAS-SMU). The SAS-SMU is a 21-item questionnaire designed by Alkis and colleagues (2017) to measure social anxiety emerging from the social media platforms. Method : In this study, a total of 842 participants within the age range of 11 to 82 years old (mean age 33.11 ± 12.134), 59% female) answered the questions in an online survey. The original version of the scale was translated into Persian using the back translation procedure. All participants completed a Demographic Questionnaire, the SAS-SMU, and the Beck Anxiety Inventory. In order to analyze the collected data, internal and external consistency, factor analysis, construct validity, and confirmatory factor analysis (CFA) were examined. A significance level of less than 0.05 was considered to determine statistical significance. Results: Four subscales were obtained from the exploratory factor analysis (SCA, PCA, IA, and SEA), which were confirmed by the confirmatory factor analysis. Cronbach's alpha for internal consistency was found to be 0.931 for the total scale and 0.920, 0.846, 0.901, 0.828 for SCA, PCA, IA, and SEA, respectively. In addition, the test-retest scores of 30 participants (interval: between 2 to 3 weeks) for all four subscales (SCA = 0.641, PCA = 0.773, IA = 0.688, SEA = 0.727) indicated acceptable stability of the questionnaire over time. Conclusion: This study validates the Persian version of the SAS-SMU for use in studies in the field of psychological problems related to social media and online communications.

Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS).

INTRODUCTION: There are numerous cases where artificial intelligence (AI) can be applied to improve the outcomes of medical education. The extent to which medical practitioners and students are ready to work and leverage this paradigm is unclear in Iran. This study investigated the psychometric properties of a Persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) developed by Karaca, et al. in 2021. In future studies, the medical AI readiness for Iranian medical students could be investigated using this scale, and effective interventions might be planned and implemented according to the results. METHODS: In this study, 502 medical students (mean age 22.66(± 2.767); 55% female) responded to the Persian questionnaire in an online survey. The original questionnaire was translated into Persian using a back translation procedure, and all participants completed the demographic component and the entire MAIRS-MS. Internal and external consistencies, factor analysis, construct validity, and confirmatory factor analysis were examined to analyze the collected data. A P ≤ 0.05 was considered as the level of statistical significance. RESULTS: Four subscales emerged from the exploratory factor analysis (Cognition, Ability, Vision, and Ethics), and confirmatory factor analysis confirmed the four subscales. The Cronbach alpha value for internal consistency was 0.944 for the total scale and 0.886, 0.905, 0.865, and 0.856 for cognition, ability, vision, and ethics, respectively. CONCLUSIONS: The Persian version of MAIRS-MS was fairly equivalent to the original one regarding the conceptual and linguistic aspects. This study also confirmed the validity and reliability of the Persian version of MAIRS-MS. Therefore, the Persian version can be a suitable and brief instrument to assess Iranian Medical Students' readiness for medical artificial intelligence.

Validation of the Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) questionnaire for adults.

BACKGROUND: The Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score". METHODS: The COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed. RESULTS: From >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex. CONCLUSIONS: COH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.

The collaborative outcomes study on health and functioning during infection times in adults (COH-FIT-Adults): Design and methods of an international online survey targeting physical and mental health effects of the COVID-19 pandemic.

BACKGROUND: . High-quality comprehensive data on short-/long-term physical/mental health effects of the COVID-19 pandemic are needed. METHODS: . The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is an international, multi-language (n=30) project involving >230 investigators from 49 countries/territories/regions, endorsed by national/international professional associations. COH-FIT is a multi-wave, on-line anonymous, cross-sectional survey [wave 1: 04/2020 until the end of the pandemic, 12 months waves 2/3 starting 6/24 months threreafter] for adults, adolescents (14-17), and children (6-13), utilizing non-probability/snowball and representative sampling. COH-FIT aims to identify non-modifiable/modifiable risk factors/treatment targets to inform prevention/intervention programs to improve social/health outcomes in the general population/vulnerable subgrous during/after COVID-19. In adults, co-primary outcomes are change from pre-COVID-19 to intra-COVID-19 in well-being (WHO-5) and a composite psychopathology P-Score. Key secondary outcomes are a P-extended score, global mental and physical health. Secondary outcomes include health-service utilization/functioning, treatment adherence, functioning, symptoms/behaviors/emotions, substance use, violence, among others. RESULTS: . Starting 04/26/2020, up to 14/07/2021 >151,000 people from 155 countries/territories/regions and six continents have participated. Representative samples of ≥1,000 adults have been collected in 15 countries. Overall, 43.0% had prior physical disorders, 16.3% had prior mental disorders, 26.5% were health care workers, 8.2% were aged ≥65 years, 19.3% were exposed to someone infected with COVID-19, 76.1% had been in quarantine, and 2.1% had been COVID 19-positive. LIMITATIONS: . Cross-sectional survey, preponderance of non-representative participants. CONCLUSIONS: . Results from COH-FIT will comprehensively quantify the impact of COVID-19, seeking to identify high-risk groups in need for acute and long-term intervention, and inform evidence-based health policies/strategies during this/future pandemics.

Physical and mental health impact of COVID-19 on children, adolescents, and their families: The Collaborative Outcomes study on Health and Functioning during Infection Times - Children and Adolescents (COH-FIT-C&A).

BACKGROUND: The COVID-19 pandemic has altered daily routines and family functioning, led to closing schools, and dramatically limited social interactions worldwide. Measuring its impact on mental health of vulnerable children and adolescents is crucial. METHODS: The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT - www.coh-fit.com) is an on-line anonymous survey, available in 30 languages, involving >230 investigators from 49 countries supported by national/international professional associations. COH-FIT has thee waves (until the pandemic is declared over by the WHO, and 6-18 months plus 24-36 months after its end). In addition to adults, COH-FIT also includes adolescents (age 14-17 years), and children (age 6-13 years), recruited via non-probability/snowball and representative sampling and assessed via self-rating and parental rating. Non-modifiable/modifiable risk factors/treatment targets to inform prevention/intervention programs to promote health and prevent mental and physical illness in children and adolescents will be generated by COH-FIT. Co-primary outcomes are changes in well-being (WHO-5) and a composite psychopathology P-Score. Multiple behavioral, family, coping strategy and service utilization factors are also assessed, including functioning and quality of life. RESULTS: Up to June 2021, over 13,000 children and adolescents from 59 countries have participated in the COH-FIT project, with representative samples from eleven countries. LIMITATIONS: Cross-sectional and anonymous design. CONCLUSIONS: Evidence generated by COH-FIT will provide an international estimate of the COVID-19 effect on children's, adolescents' and families', mental and physical health, well-being, functioning and quality of life, informing the formulation of present and future evidence-based interventions and policies to minimize adverse effects of the present and future pandemics on youth.

Prediction of Anger Expression of Individuals with Psychiatric Disorders using the Developed Computational Codes based on the Various Soft Computing Algorithms.

Anger is defined as a psychobiological emotional state that consists of feelings varying in intensity from mild irritation or annoyance to intense fury and rage. Dysfunction in anger regulation is marker of most psychiatric disorders. The most important point about anger regulation by the individuals is how to express anger and control it. The purpose of the present study is to predict the anger expression from the anger experience in individuals with psychiatric disorder for assessment of how to express and control the anger. To this end, the number of 3,000 subjects of individuals with clinical disorders had filled in the State-Trait Anger Expression Inventory-II (STAXI-II). After removing the uncertain diagnoses (900 subjects), the number of 2,100 data was considered in the analysis. Then, the computational codes based on three soft computing algorithms, including Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Decision Tree (DT) were developed to predict the scales of anger expression of the individuals with psychiatric disorders. The scales of anger experience were used as input data of the developed computational codes. Comparison between the results obtained from the DT, RBF and ANFIS algorithms show that all the developed soft computing algorithms forecast the anger expression scales with an acceptable accuracy. However, the accuracy of the DT algorithm is better than the other algorithms.

Prediction of anger expression of individuals with psychiatric disorders using the developed computational codes based on the various soft computing algorithms

Anger is defined as a psychobiological emotional state that consists of feelings varying in intensity from mild irritation or annoyance to intense fury and rage. Dysfunction in anger regulation is marker of most psychiatric disorders. The most important point about anger regulation by the individuals is how to express anger and control it. The purpose of the present study is to predict the anger expression from the anger experience in individuals with psychiatric disorder for assessment of how to express and control the anger. To this end, the number of 3,000 subjects of individuals with clinical disorders had filled in the State-Trait Anger Expression Inventory-II (STAXI-II). After removing the uncertain diagnoses (900 subjects), the number of 2,100 data was considered in the analysis. Then, the computational codes based on three soft computing algorithms, including Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Decision Tree (DT) were developed to predict the scales of anger expression of the individuals with psychiatric disorders. The scales of anger experience were used as input data of the developed computational codes. Comparison between the results obtained from the DT, RBF and ANFIS algorithms show that all the developed soft computing algorithms forecast the anger expression scales with an acceptable accuracy. However, the accuracy of the DT algorithm is better than the other algorithms

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Tear Film Break-up Time in Bipolar Disorder.

OBJECTIVE: Ocular dryness is a well-recognized adverse side effect of many topical and systemic medications. In psychiatry, patients who have consumed such drugs as lithium carbonate and sodium valproate frequently experience dry eye symptoms. The purpose of this study was to compare tear film stability between patients who use these drugs with those patients who are not on medication. METHODS: After obtaining informed consent, the tear film break up time (TBUT) test was performed in 96 eyes of 48 subjects. The subjects were placed in to three groups. Participants included two groups of euthymic bipolar disorder patients (16 cases each) with history of pharmacotherapy for more than two years. Patients in each group were taking only one type of mood stabilizer (lithium carbonate or sodium valproate). In addition,16 age-matched bipolar patients who did not take any topical or systemic medications were included in a control group. Values of TBUT were compared between the three groups using the student's t-test. RESULTS: The mean tear film break up time (TBUT) in test groups were 4.88 seconds± 0.34(lithium group), 4.81 seconds ± 1.60 (valproate group) and 15seconds± 2.0(control group), respectively. No statistically significant differences were observed between the first and the second groups in TBUT values, but significant differences were found between the two groups and the control group (P< 0.0001). CONCLUSION: The results of this study show that lithium carbonate and sodium valproate contribute to decrease of tear film break up time, resulting from dryness of the eyes.