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The transition into menopause marks a significant stage in a woman's life, indicating the end of reproductive capability. This period, encompassing perimenopause and menopause, is characterized by declining levels of estrogen and progesterone, leading to various symptoms such as hot flashes, sleep disturbances, sexual dysfunction, and mood irregularities. Moreover, cognitive functions, notably memory, may decline during this phase. This exploratory study aimed to evaluate psychological factors in a sample of 98 women recruited from a diagnostic-assistance hospital pathway (AOUP). Psychological variables, including depression, anxiety, stress, sleep quality, memory, personality traits, and mindfulness, were assessed using psychometric questionnaires. Machine learning techniques were employed to identify independent variables strongly correlated with higher levels of depression measured by BDI-II. The findings revealed positive associations between depression and anxiety, stress, low mood, poor sleep quality, and memory complaints, while mindfulness showed a negative correlation. Remarkably, the machine learning analysis achieved a high classification accuracy in distinguishing between individuals with different levels of depression (low vs high). These results underscore the importance of addressing psychological factors during menopause and offer valuable insights for future research and the development of targeted clinical interventions aimed at enhancing mental health and quality of life for women during this transitional phase.

\"An engaging, practical guide to psychological testingEssentials of Psychological Testing, 2nd Edition is a fully updated and revised overview of the basic principles of psychometrics, including the information needed to understand and evaluate tests. It contains everything you need to know about the science of measuring knowledge, abilities, attitudes, personality traits, and education level, and introduces readers to the most relevant reference works in the field. This guide was written specifically for active professionals and students in fields ranging from clinical mental health and social work to education and human resources. In Essentials of Psychological Testing, 2nd Edition, even the most complicated psychometric principles are made interesting and easy to comprehend. More importantly, you will learn how to translate this new knowledge into practice by selecting the right tests, administering them appropriately, and correctly interpreting results.Dr. Susana Urbina describes the science of psychometrics, along with its history, providing important background information for the sections that follow. You'll find detailed information on testing procedures, score interpretation, reliability and validity, and \"Test Yourself\" questions that help activate your knowledge. In addition, the Second Edition has been updated to include: Information from the revised Standards for Educational and Psychological Testing New examples from the most recent versions of psychological tests Discussion of new test administration technologies, such as the Q-Interactive Illustrations and easy-to-find reinforcement of key concepts Dr. Urbina is recognized as a leading expert in the field of psychometrics, and her enjoyable writing style makes it easy to master this complex subject. Essentials of Psychological Testing, 2nd Edition is required reading for anyone who needs to know what psychological tests are and how to use them in a professional, consistent, scientifically sound manner\"-- Provided by publisher.

Background Antenatal depression has become a common and serious problem, significantly affecting maternal and fetal health. However, evaluation and intervention methods for pregnant women in obstetric clinics are inadequate. This study aimed to determine the prevalence of and risk factors for depression among all pregnant women at their first attending antenatal care in the obstetrics clinic, a comprehensive teaching hospital, southwest of China. Methods From June to December 2019, 5780 pregnant women completed online psychological assessments, and data from 5728 of the women were analyzed. The women were categorized into two groups according to the presence or absence of depression. Depression was assessed by the Patient Health Questionnaire-9 (PHQ-9), with a cutoff point of 10 for depression. Anxiety and somatic symptoms were measured by the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-15 (PHQ-15), respectively. Univariate analysis and binary logistic regression analysis were used to determine the association among antenatal depression, anxiety, somatic symptoms and participants’ characteristics. Results The prevalence of antenatal depression among all the pregnant women at their first attending antenatal care was 16.3%, higher in the first trimester (18.1%). Anxiety symptoms (Mild anxiety AOR = 2.937; 95% CI: 2.448–3.524) and somatic symptoms (Mild somatic symptoms AOR = 3.938; 95% CI: 2.888–3.368) were major risk factors for antenatal depression among women and the risk increased more with the anxiety level or somatic symptoms level. Gestational weeks (second trimester AOR = 0.611; 95% CI: 0.483–0.773; third trimester AOR = 0.337; 95% CI: 0.228–0.498) and urban residence (AOR = 0.786; 95% CI: 0.652–0.947) were protective factors for antenatal depression among women. Conclusions About one in six pregnant women would experience depression, and special attention should be paid to some risk factors (i.e., early pregnancy, anxiety symptoms, somatic symptoms, rural residence). Online psychological assessments might be a time-saving and convenient screening method for pregnant women in obstetric clinics.

\"As the world has become hyper-connected, people have become hyper-skeptical, and communication has actually gotten much harder. As connected as we are, attention spans are shrinking, trust is dwindling, and growth is less guaranteed. An exciting new branch of artificial intelligence--Personality AI--is setting out to change that: marrying traditional machine learning, data analytics, and behavioral psychology, Personality AI helps professional communicators break through the noise with empathy and build trust with their audience, leveraging data to accelerate trust, build relationships, and win more customers. The proposed title is the playbook for anyone whose livelihood or business is built on their ability to communicate effectively and build teams. Exploring the next generation of human communication, Crystal co-founder and CEO Drew D'Agostino exposes how businesses can reap the benefits of AI and machine learning today, not tomorrow. Readers will not only learn what Personality AI is and how it works, but direct applications in business, life, and how to understand personality types in context--sales, recruiting, coaching, and more. D'Agostino also presents important considerations that using Personality AI brings up in ethics and compliance conversations, plus guidelines for any company that wants to use their people data to learn and execute . This book provides practical guidelines for using this technology to accelerate growth, strengthen relationships, communicate more effectively, and win more business\"-- Provided by publisher.

The COVID-19 pandemic has brought to the fore the question of whether psycho-legal assessments can be executed remotely in a manner that adheres to the rigorous standards applied during in-person assessments. General guidelines have evolved, but to date, there are no explicit directives about whether and how to proceed. This paper reviews professional, ethical, and legal challenges that experts should consider before conducting such an evaluation remotely. Although the discussion is more widely applicable, remote forensic psychological assessment of adults alleging childhood abuse is used as an example throughout, due to the complexity of these cases, the ethical dilemmas they can present, and the need to carefully assess non-verbal trauma-related symptoms. The use of videoconferencing technology is considered in terms of potential benefits of this medium, as well as challenges this method could pose to aspects of interviewing and psychometric testing. The global pandemic is also considered with respect to its effects on functioning and mental health and the confounding impact such a crisis has on assessing the relationship between childhood abuse and current psychological functioning. Finally, for those evaluators who want to engage in remote assessment, practice considerations are discussed.

Background/Objectives: Psychological vulnerability in individuals with obesity represents a significant concern in the context of bariatric surgery. This study aimed to assess psychosocial functioning and identify the psychological, clinical, and sociodemographic predictors of impairment among patients undergoing preoperative evaluation. Methods: A retrospective observational study was conducted on patients referred for bariatric surgery at a single academic medical center. Data were collected through clinical interviews and validated psychometric tools: the Clinical Impairment Assessment (CIA), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7). Robust multiple regression analysis determined associations between CIA scores and psychological and demographic factors. Results: A total of 688 patients were evaluated (median age: 46 years; 70.3% female). Most had a high school education (56.9%) and were employed (69%). Elevated scores on the Clinical Impairment Assessment (CIA) were significantly associated with female gender (β = 1.075, p = 0.029), moderate anxiety (GAD-7 ≥ 10; β = 3.85, p < 0.001), and severe depressive symptoms (PHQ-9 ≥ 15; β = 16.67, p < 0.001). Other significant predictors included prior psychotherapy (β = 1.18, p = 0.044), aesthetic motivation for surgery (β = 0.92, p = 0.120), and expectations that weight loss would improve self-esteem (β = 2.11, p = 0.001) or social relationships (β = 1.98, p = 0.002). Conversely, physical activity was associated with lower CIA scores (β = –1.23, p = 0.050). The regression model showed strong explanatory power (McFadden R2 = 0.529). Conclusions: This study highlights key predictors of psychosocial distress in bariatric candidates, underscoring the importance of comprehensive psychological assessment before surgery. The CIA appears to be a valuable screening and monitoring tool. Future research should explore the longitudinal evolution of psychosocial functioning and support the integration of psychological care into multidisciplinary bariatric programs.

\"Making schemas understandable to those working with or looking after young children, this book describes schematic behaviours with case studies and provides ideas of how to use this information to extend children's learning and development\" -- From the publisher.

Abstract Background Depression, as a globally prevalent mental disorder, has individual differences and insufficient targeted intervention measures in its nursing pathway, which leads to low treatment compliance and high recurrence rate among patients with depression. However, existing research heavily relies on empirical nursing plans and lacks systematic integration and analysis of multidimensional patient data, making it difficult to achieve precise nursing care. In order to improve the nursing intervention effect on patients with depression, computer data analysis technology was used in the study. By integrating clinical indicators, psychological assessment results, and lifestyle behavior data of patients with depression, a personalized nursing pathway optimization model was developed, providing new methodological ideas for improving the quality of nursing care for patients with depression. Methods The study selected 200 patients with depression admitted to a psychiatric hospital from January 2024 to January 2025 as the research subjects. According to the principle of balanced baseline data, the patients with depression were divided into an experimental group (100 cases) and a control group (100 cases). Among them, the control group received routine depression care pathways, including medication guidance, psychological counseling, and regular follow-up. On the basis of routine nursing, the experimental group optimized the nursing pathway through computer data analysis. The specific steps are as follows: ① Data collection: Firstly, extract general information of patients with depression through the hospital information system. Secondly, the Hamilton Depression Scale (HAMD) and Self rating Depression Scale (SDS) were used to collect psychological assessment data. Finally, use smart wristbands to collect lifestyle behavior data such as sleep duration and exercise steps from patients. ② Data analysis: Use Python data analysis tools to clean and integrate the collected data, and use random forest algorithm to construct a nursing effect prediction model. Then, optimize nursing intervention measures in reverse based on the output results of the model. ③ Observation indicators: The total intervention duration is 8 weeks, and the observation time nodes include before intervention, 4 weeks after intervention, and 8 weeks after intervention. The observed indicators are the HAMD score and SDS score of the two groups of patients. Results After 8 weeks of intervention, the HAMD score of the experimental group was 12.34 ± 3.52, significantly lower than the control group's 18.67 ± 4.22, and the difference was statistically significant (t = 11.519, p<.001). Meanwhile, the SDS score of the experimental group was 45.87 ± 5.12, which was significantly lower than the control group's 56.27 ± 6.36 (t = 12.738, p<.001). In addition, there was a significant difference (p<.05) in the HAMD and SDS scores of the experimental group before and after intervention. Discussion It can be seen that optimizing the nursing path for patients with depression through computer data analysis can effectively reduce their depression scores and improve the effectiveness of nursing interventions. This plan integrates multidimensional data of patients to achieve personalized adjustment of nursing interventions, filling the limitations of traditional nursing pathways. Future research can combine artificial intelligence technology to optimize data analysis models and further enhance the dynamic adjustment capability of nursing plans.