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Introduction and objective: Numerous studies have found that doctors are exposed to stress and burnout at work. In consequence, these processes lead to a decrease in the quality of life. The study aimed to determine whether professional burnout, understood as a cognitive existential process, is related to stress at work and if any of the four dimensions of burnout are its predictor. The study also analyzed relations between organizational-professional and non-professional-social factors and burnout and stress at work. Material and methods: 318 doctors (210 women, 108 men), aged 27 – 8, participated in the study. Link Burnout Questionnaire (LBQ) was used to measure burnout, and the Perceived Stress Scale (PSS-10) to measure the variable describing the current level of stress. Results: Occupational burnout is related to stress. Two of its symptoms: exhaustion and lack of a sense of professional effectiveness, are important predictors for the sense of stress experienced by the examined group of doctors. The individuals who are in a partner relationship (but not marriage) experienced a stronger sense of non-professional effectiveness than those who were single. Being in an informal relationship is related to the level of stress and lack of a sense of professional effectiveness. The level of stress, as well as all burnout indicators, are connected with the function of a team leader and having passion. The frequency of taking leave and using social networks is related to the level of stress and burnout. Conclusions: Problems related to burnout in doctors, and therefore people professionally involved in helping and treating, must not be underestimated, as evidenced by the results of the presented study Professional burnout of doctors leads to somatic and psychological problems. Doctors suffering from occupational burnout need support and psychological assistance the same as any other professional group.

Mental health conditions currently account for approximately 14 percent of the global disease burden, according to the World Health Organization, while depression will be the leading cause of disability by 2030. This developing crisis underlines the pressing need to ascertain behavioral and psychological markers associated with mental health risks. This study investigates psychosocial determinants of self-reported mental health history by analyzing a large-scale publicly available behavioral dataset comprising 292,364 individual observations. Artificial Intelligence (AL) based machine learning logistics Modeling framed in this study with self-reported mental status as response variable and four key psychosocial predictors: Mood Swings, Coping Struggles, Work Interest and Social Weakness as independent variables. Initial bivariate models indicated significant associations between Mood Swings and Coping Struggles and the likelihood of reporting mental health conditions. In the final logistic model, Mood Swings (p < 2e-16, T value: 17.61) and Work Interest (p < 2e-16, T value: 31.48) emerged as the most significant positive predictors, while Social Weakness showed a statistically significant negative association. VIF scores indicated no multicollinearity among the predictors. Further, gender-stratified modeling framed by this current study showed striking differences in predictor behavior between male and female respondents; this is especially true for social functioning, which appeared positively significant in females but negatively significant in males, and for coping mechanisms, which appeared much stronger in females. These findings point toward the important role of emotional regulation, managing of stress, vocational engagement, and interpersonal dynamics in shaping mental health outcomes. This brings into evidence the optimal threshold on the ROC curve, depicting that about 70.4% of the actual positive cases of mental health are correctly identified by the model. The logistic model slightly outperformed and revealed that AI can be used to predict mental health risks.

The COVID-19 pandemic is a stressful long-lasting event with an increasingly negative impact upon individuals. This study aimed at assessing the magnitude of depression, anxiety, and stress among adults living in Portugal during the first mandatory lockdown of 2020, and the psychosocial and health-related factors associated with these symptoms. A sample of 484 adults (73% women) with an average age of 40 years old (Standard Deviation, SD = 14.03) responded to an online survey. The survey included measures of depression, anxiety, stress, social support, COVID-19 interference in daily life, attitudes towards COVID-19, and health perception. The impact of the lockdown on psychological well-being was large, with up to 36% of the participants showing signs of at least mild psychological discomfort (i.e., depression, anxiety, and stress). Social support, COVID-19 interference on daily life, health perception, and age, explained all the dependent variables. Education level, income, attitudes towards COVID-19, and gender explained some of the dependent variables. These results suggest that the COVID-19 pandemic has a serious impact on the psychological health of Portuguese adults. The role of the procedures to control the pandemic on the mental health of Portuguese adults should not be underestimated.

This study investigated the corresponding change between psycho-social predictors and physical activity (PA) behavior and if these relationships were dependent on the stages of change from the Transtheoretical Model in Minority American adolescents. We conducted a longitudinal field study with N = 357 students aged 13–18 years (M = 14.24 years, SD = 0.88); predominantly Filipino (61.2%) using a test–retest design assessing psycho-social PA predictors (enjoyment, self-efficacy, family support, friends’ support, knowledge, stage of change) and moderate-to-vigorous physical activity (MVPA) at two time points over six months. Hierarchical regression results indicated that a positive change of enjoyment, knowledge about PA and family support predicted a change of MVPA, independently of stage. The time-varying covariation showed the importance of the current stage of change for enjoyment, self-efficacy and support of friends for a change of MVPA. Overall, our findings suggest that an individual’s current stage of change should be considered to determine individually appropriate starting points and goals for designing interventions to promote PA among Minority American adolescents.

To reduce the environmental impact of food production has spurred the development of digital tools to promote sustainable eating, yet little research examined the psychosocial predictors of their adoption. This study addresses this gap by applying the Unified Theory of Acceptance and Use of Technology, including performance expectancy, effort expectancy, social influence, and facilitating conditions as predictors of technology use. To enhance the model's predictiveness, we included variables from the Model of Goal-Directed Behavior-attitude, anticipated emotions, and desire- as well as sociodemographic factors. 511 participants completed an online survey in which they evaluated a customized app designed to promote sustainable eating. Results showed that performance expectancy was the strongest predictor of attitude, anticipated emotions, and intention, whereas effort expectancy was not. Social influence and facilitating conditions predicted desire, which in turn predicted intention, and thus led to app download. Facilitating conditions and emotions were more influencial for women, older participants showed a higher desire despite high effort expectation, and those with higher education levels downloaded the app more due its perceived ease of use. These findings suggest that developers should focus on enhancing the emotional appeal and perceived value of the app while addressing the users' diverse needs based on gender, age, and education.

Background Changes to sexual wellbeing are acknowledged to be a long-term negative consequence of cancer and cancer treatment. These changes can have a negative effect on psychological well-being, quality of life and couple relationships. Whilst previous conclusions are based on univariate analysis, multivariate research can facilitate examination of the complex interaction between sexual function and psycho-social variables such as psychological wellbeing, quality of life, and relationship satisfaction and communication in the context of cancer, the aim of the present study. Method Six hundred and fifty seven people with cancer (535 women, 122 men) and 148 partners (87 women, 61 men), across a range of sexual and non-sexual cancers, completed a survey consisting of standardized measures of sexual functioning, depression and anxiety, quality of life, relationship satisfaction, dyadic sexual communication, and self-silencing, as well as ratings of the importance of sex to life and relationships. Results Men and women participants, reported reductions in sexual functioning after cancer across cancer type, for both people with cancer and partners. Multiple regression analysis examined psycho-social predictors of sexual functioning. Physical quality of life was a predictor for men and women with cancer, and for male partners. Dyadic sexual communication was a predictor for women with cancer, and for men and women partners. Mental quality of life and depression were also predictors for women with cancer, and the lower self-sacrifice subscale of self-silencing a predictor for men with cancer. Conclusion These results suggest that information and supportive interventions developed to alleviate sexual difficulties and facilitate sexual renegotiation should be offered to men and women with both sexual and non-sexual cancers, rather than primarily focused on individuals with sexual and reproductive cancers, as is the case currently. It is also important to include partners in supportive interventions. Interventions aimed at improving sexual functioning should include elements aimed at improving physical quality of life and sexual communication, with a focus on psychological wellbeing also being important for women with cancer.

Objectives To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning. Results Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ2 17 = 29.4, P = 0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic = 0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients’ social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary. Conclusions The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models. What is known about the topic? Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions. What does this paper add? This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression. What are the implications for practitioners? The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary.

This study aimed to construct interpretable machine learning models to predict family intimacy in cancer patients and identify the most influential predictors through SHAP-based analysis. A total of 259 cancer patients were surveyed. The data cleaning process involved handling missing values, normalizing continuous variables, and applying one-hot encoding to categorical variables. Statistically significant sociodemographic variables (age, marital status, education, and income) and psychosocial attributes (self-esteem and three resilience subdimensions: tenacity, strength, and optimism) were selected using LASSO regression. Four regression models--gradient boosting (GB), random forest (RF), XGBoost (XGB), and decision tree (DT)--were trained and evaluated using R[sup.2], mean-squared error (MSE), and mean absolute percentage error (MAPE).SHapley Additive exPlanations (SHAP) was used to interpret the GB model. The GB model achieved the best predictive performance (R[sup.2]=0.6985, MSE=0.2405), followed by XGB (R[sup.2]=0.6794), RF (R[sup.2]=0.6653), and DT (R[sup.2]=0.5912). SHAP analysis revealed that psychological variables--tenacity, strength, and self-esteem--were the most influential predictors, all exerting strong positive effects. Age group and education showed moderate impact, while income, gender, and marital status contributed minimally. Gradient boosting offers a robust and interpretable framework for predicting family intimacy in cancer patients. Positive psychological resources--especially resilience and self-esteem--outperform traditional demographics as a predictive foundation, highlighting their clinical significance in survivorship care planning.

While significant weight loss occurs post-bariatric surgery, partial weight regain is common. Psychological and dispositional variables have been examined as predictors of weight change, but most studies have focused on the relationship of preoperative constructs to shorter-term postoperative outcomes. The goal of the current study was to examine associations between weight loss and postoperative psychosocial and behavioral factors up to an average of 13.7 years after surgery. The current study was conducted at a large bariatric center in a Midwestern U.S. city. The sample was comprised of 125 adult patients who participated in the second wave of a long-term bariatric surgery outcome study, examining weight history, physical activity, and psychological health and functioning. Correlations between percent total weight loss (%TWL) and psychosocial and behavioral variables were examined. The variables that had significant correlations with %TWL were used in stepwise linear regressions to determine their contribution to %TWL. These same variables were tested to determine differences among those in the highest and lowest weight loss quartiles. Life satisfaction, conscientiousness, positive affect, and regular exercise were positively associated with weight loss in the entire sample and were significantly higher among those in the highest versus the lowest weight-loss quartile. Experiencing a stressful event and food addiction symptoms were negatively associated with weight loss. Positive affect, fewer food addiction symptoms, and regular exercise significantly predicted weight loss, accounting for 23% of the variance in %TWL. Long-term weight loss maintenance after bariatric surgery may be related to positive affect, conscientiousness, regular physical activity, and an addictive-type relationship with food. Future studies should explore these relationships and develop approaches to deal with the interaction between dispositional tendencies and lifestyle factors.

Early commencement of antiretroviral treatment can be beneficial and economical in the long run. Despite global advances in access to care, a significant proportion of adults presenting at HIV/AIDS care facilities present with advanced HIV disease. Understanding factors associated with late presentation for HIV/AIDS services is critical to the development of effective programs and treatment strategies. Literature on factors associated with late presentation for an HIV diagnosis is reviewed. Highlighted is the current emphasis on socio-demographic factors, the limited exploration of psychosocial correlates, and inconsistencies in the definition of late presentation that make it difficult to compare findings across different studies. Perspectives based on experiences from resource limited settings are underreported. Greater exploration of psychosocial predictors of late HIV diagnosis is advocated for, to guide future intervention research and to inform public policy and practice targeted at ‘difficult to reach’ populations.