Exploring the impact of depression, anxiety, stress, academic engagement, and dropout intention on medical students' academic performance: A prospective study.

BACKGROUND: Depression, anxiety, and stress (DAS) have been linked to poor academic outcomes. This study explores the relationships among DAS, academic engagement, dropout intentions, and academic performance - measured by Grade Point Average (GPA) - in medical students. It aims to understand how these factors relate to each other and predict academic performance. METHODS: Data were collected from 351 medical students (74.9 % female) through an online survey. The average age was 20.2 years. Psychometric instruments measured DAS, academic engagement, and dropout intentions. Structural equation modeling was used to test the relationships between these variables and their prediction of GPA. RESULTS: DAS was negatively associated with academic engagement ß̂=-0.501p<0.001 and positively associated with dropout intentions ß̂=0.340p<0.001. Academic engagement positively predicted GPA ß̂=0.298p<0.001 and negatively associated with dropout intentions ß̂=-0.367p<0.001. DAS had a nonsignificant direct effect on GPA ß̂=-0.008p=0.912. However, the indirect effect of DAS - via academic engagement - on GPA and dropout intention was statistically significant. LIMITATIONS: The study's limitations include the use of a convenience sample and the collection of all variables, except GPA, at the same time point, which may affect the generalizability of the results. CONCLUSIONS: The study supports the important role of DAS in its association with academic engagement and dropout intentions, which can predict GPA. Addressing DAS could enhance academic engagement and reduce dropout rates, leading to better academic performance.

Rates and predictors of service disengagement in first episode psychosis: Results from a 2-year follow-up study in an Italian real-world care setting.

AIM: Service disengagement is a major problem for "Early Intervention in Psychosis" (EIP). Understanding predictors of engagement is also crucial to increase effectiveness of mental health treatments, especially in young people with First Episode Psychosis (FEP). No Italian investigation on this topic has been reported in the literature to date. The goal of this research was to assess service disengagement rate and predictors in an Italian sample of FEP subjects treated within an EIP program across a 2-year follow-up period. METHODS: All patients were young FEP help-seekers, aged 12-35 years, recruited within the "Parma Early Psychosis" (Pr-EP) program. At baseline, they completed the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF) scale. Univariate and multivariate Cox regression analyses were carried out. RESULTS: 489 FEP subjects were enrolled in this study. Across the follow-up, a 26 % prevalence rate of service disengagement was found. Particularly strong predictors of disengagement were living with parents, poor treatment adherence at entry and a low baseline PANSS "Disorganization" factor score. CONCLUSION: More than a quarter of our FEP individuals disengaged the Pr-EP program during the first 2 years of intervention. A possible solution to reduce disengagement and to facilitate re-engagement of these young patients might be to offer the option of low-intensity monitoring and support, also via remote technology and tele-mental health care.

Attrition of older adults in web-based health interventions: Survival analysis within an observational cohort study.

To identify demographics and personal motivation types that predict dropping out of eHealth interventions among older adults. We conducted an observational cohort study. Participants completed a pre-test questionnaire and got access to an eHealth intervention, called Stranded, for 4 weeks. With survival and Cox-regression analyses, demographics and types of personal motivation were identified that affect drop-out. Ninety older adults started using Stranded. 45.6% participants continued their use for 4 weeks. 32.2% dropped out in the first week and 22.2% dropped out in the second or third week. The final multivariate Cox-regression model which predicts drop-out, consisted of the variables: perceived computer skills and level of external regulation. Predicting the chance of dropping out of an eHealth intervention is possible by using level of self-perceived computer skills and level of external regulation (externally controlled rewards or punishments direct behaviour). Anticipating to these factors can improve eHealth adoption.

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems.

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

Crowd Counting Using Meta-Test-Time Adaptation.

Machine learning algorithms are commonly used for quickly and efficiently counting people from a crowd. Test-time adaptation methods for crowd counting adjust model parameters and employ additional data augmentation to better adapt the model to the specific conditions encountered during testing. The majority of current studies concentrate on unsupervised domain adaptation. These approaches commonly perform hundreds of epochs of training iterations, requiring a sizable number of unannotated data of every new target domain apart from annotated data of the source domain. Unlike these methods, we propose a meta-test-time adaptive crowd counting approach called CrowdTTA, which integrates the concept of test-time adaptation into the meta-learning framework and makes it easier for the counting model to adapt to the unknown test distributions. To facilitate the reliable supervision signal at the pixel level, we introduce uncertainty by inserting the dropout layer into the counting model. The uncertainty is then used to generate valuable pseudo labels, serving as effective supervisory signals for adapting the model. In the context of meta-learning, one image can be regarded as one task for crowd counting. In each iteration, our approach is a dual-level optimization process. In the inner update, we employ a self-supervised consistency loss function to optimize the model so as to simulate the parameters update process that occurs during the test phase. In the outer update, we authentically update the parameters based on the image with ground truth, improving the model's performance and making the pseudo labels more accurate in the next iteration. At test time, the input image is used for adapting the model before testing the image. In comparison to various supervised learning and domain adaptation methods, our results via extensive experiments on diverse datasets showcase the general adaptive capability of our approach across datasets with varying crowd densities and scales.

The forecasting of surface displacement for tunnel slopes utilizing the WD-IPSO-GRU model.

To quickly assess slope stability based on field displacement monitoring data, this paper constructs a hybrid optimization model that predicts surface displacement during tunnel excavation in base-overburden slopes. The model combines Wavelet Decomposition (WD) with a Gated Recurrent Unit (GRU), and the GRU's hyperparameters are optimized using an Improved Particle Swarm Optimization algorithm (IPSO). The specific steps are as follows: First, the Wavelet Decomposition (WD) technique is applied to decompose the raw displacement data, extracting features at different time-frequency scales. Next, the Dropout technique is incorporated into the GRU model to prevent overfitting. Additionally, nonlinear inertia weight ω improved cognitive factor c1, and social factor c2 are introduced. The PSO algorithm is improved by integrating crossover and mutation concepts from genetic algorithms. Finally, the IPSO is used to optimize the number of neural units hN, HN, LN and dropout rates D1 and D2 in the GRU network architecture. After constructing the WD-IPSO-GRU model, a comprehensive comparison is made with various swarm intelligence algorithms and state-of-the-art models. The experimental results demonstrate that the WD-IPSO-GRU model significantly improves the prediction accuracy of surface displacement in slopes during tunnel excavation. Compared to directly using raw data for prediction, the introduction of the WD preprocessing technique improved the prediction accuracy at measurement points 01 and 02 by 28% and 45.9%, respectively. Additionally, with the model optimized by IPSO, the prediction accuracy at measurement points 01 and 02 increased by 76% and 56.7%, respectively. The WD-IPSO-GRU model effectively addresses the challenges of extracting features from univariate displacement time-series data and determining the parameters of the GRU network. It improves the prediction accuracy of surface displacement in base-overburden type slopes and demonstrates excellent generalization ability and reliability. The research results validate the potential application of the model in geotechnical engineering and provide strong support for assessing slope stability during tunnel excavation.

Cluster Analysis in Victims of Perceived Cyberbullying at the Higher Education Level.

A number of studies have demonstrated the prevalence of cyberbullying in university settings. The objective of this research is to conduct a cluster analysis to categorize victims according to the nature of the behavior they have received and to examine the relationship between gender and intention to drop out. To this end, the Online Victimization Questionnaire was administered to a sample of 800 first-year students at a university in northern Spain who had opted to participate in the study. All analyses were conducted using the SPSS statistical software, version 27.0. Results indicate the presence of four clusters: Cluster 4 (73.625%) exhibited no instances of cyberbullying behaviors. Cluster 1 (21.875%), which exhibited low scores across all cyberbullying behaviors except identity manipulation, was the most prevalent. Cluster 2 (3.125%) demonstrated high scores for public aggression and social isolation. Finally, Cluster 3 (1.375%) exhibited high scores for all cyberbullying behaviors. Furthermore, gender differences play a significant role in the formation of these clusters. It is therefore evident that there are various profiles of cyberbullying victims, which both public policies and educational programs should be aware of in order to adapt their prevention strategies. This is also a factor that affects university dropout prevention programs.

No medication prescription and residential distance from the hospital are important factors associated with nonsurgical weight-loss treatment discontinuance in Japanese patients with high-degree obesity: a retrospective study.

BACKGROUND: Although the percentage of the population with a high degree of obesity (body mass index [BMI] ≥ 35 kg/m2) is low in Japan, the prevalence of obesity-related diseases in patients with high-degree obesity is greater than that in patients with a BMI < 35 kg/m2. Therefore, treatment for high-degree obesity is important. However, clinical studies have reported that 20-50% of patients with obesity discontinue weight-loss treatment in other countries. The circumstances surrounding antiobesity agents are quite different between Japan and other countries. In this study, we investigated the predictors of treatment discontinuation in Japanese patients with high-degree obesity. METHODS: We retrospectively reviewed the medical charts of 271 Japanese patients with high-degree obesity who presented at Toho University Sakura Medical Center for obesity treatment between April 1, 2014, and December 31, 2017. The patients were divided into non-dropout and dropout groups. Patients who discontinued weight-loss treatment within 24 months of the first visit were defined as "dropouts." Multivariate Cox proportional hazards regression analysis and Kaplan-Meier survival analysis were performed to examine the factors predicting treatment withdrawal. RESULTS: Among the 271 patients, 119 (43.9%) discontinued treatment within 24 months of the first visit. The decrease in BMI did not significantly differ between the two groups. No prescription of medication and residential distance from the hospital exceeding 15 km were the top contributors to treatment discontinuation, and the absence of prescription medication was the most important factor. The dropout-free rate was significantly higher in patients with medication prescriptions than in those without and in patients who lived within 15 km of the hospital than in those who lived farther than 15 km from the hospital. CONCLUSIONS: No medication prescription and longer residential distance from the hospital were associated with treatment dropout in Japanese patients with high-degree obesity; therefore, the addition of antiobesity medications and telemedicine may be necessary to prevent treatment discontinuation in such patients.

Gray matter volume differences in intimate partner violence perpetrators and its role in explaining dropout and recidivism.

AIM: Psychological instruments that are employed to adequately explain treatment compliance and recidivism of intimate partner violence (IPV) perpetrators present a limited ability and certain biases. Therefore, it becomes necessary to incorporate new techniques, such as magnetic resonance imaging (MRI), to be able to surpass those limitations and measure central nervous system characteristics to explain dropout (premature abandonment of intervention) and recidivism. METHOD: The main objectives of this study were: 1) to assess whether IPV perpetrators (n = 60) showed differences in terms of their brain's regional gray matter volume (GMV) when compared to a control group of non-violent men (n = 57); 2) to analyze whether the regional GMV of IPV perpetrators before starting a tailored intervention program explain treatment compliance (dropout) and recidivism rate. RESULTS: IPV perpetrators presented increased GMV in the cerebellum and the occipital, temporal, and subcortical brain regions compared to controls. There were also bilateral differences in the occipital pole and subcortical structures (thalamus, and putamen), with IPV perpetrators presenting reduced GMV in the above-mentioned brain regions compared to controls. Moreover, while a reduced GMV of the left pallidum explained dropout, a considerable number of frontal, temporal, parietal, occipital, subcortical and limbic regions added to dropout to explain recidivism. CONCLUSIONS: Our study found that certain brain structures not only distinguished IPV perpetrators from controls but also played a role in explaining dropout and recidivism. Given the multifactorial nature of IPV perpetration, it is crucial to combine neuroimaging techniques with other psychological instruments to effectively create risk profiles of IPV perpetrators.

Meibomian Gland Dysfunction and Dropout in Diabetic Patients with Non-Proliferative Diabetic Retinopathy.

This study aims to compare meibomian gland (MG) dropout and MG dysfunction (MGD) between patients with diabetes mellitus (DM) with moderate-severe non-proliferative diabetic retinopathy (NPDR) and patients with no diabetes (NDM). This prospective, transversal, age, and gender-matched case-control study included 98 DM and 106 NDM eyes. Dry eye disease (DED) and MGD evaluations were performed, including meibography (Keratograph 5M®). The objective MG dropout percentage was obtained by analyzing meibography images with ImageJ software (v. 1.52o, National Institutes of Health, Bethesda, MD, USA) and was subsequently graded with Arita's meiboscore. The DM duration was 18 ± 9 years. The mean meiboscore (3.8 ± 0.8 vs. 3.4 ± 1.0, p = 0.001), meiboscore severity (p = 0.016), and MG dropout (45.1 ± 0.1% vs. 39.0 ± 0.4%, p < 0.001) were greater in DM than in NDM. All patients showed MG dropout (meiboscore > 1). Lower eyelids showed greater MG dropout in both groups. A correlation with age (r = 0.178, p = 0.014) and no correlations with DM duration or gender (p > 0.005) were observed. Patients with diabetes showed greater corneal staining (1.7 ± 1.3 vs. 0.9 ± 1.1; p < 0.001), reduced corneal sensitivity (5.4 ± 1.1 vs. 5.9 ± 0.4; p < 0.001), lower MG expressibility (3. 9 ± 1.6 vs. 4.4 ± 2.1; p = 0.017), and worse meibum quality (1.9 ± 0.8 vs. 1.7 ± 0.5; p = 0.019). Tear breakup time, osmolarity, MMP-9, Schirmer, and the Ocular Surface Disease Index showed no significant differences. In conclusion, patients with DM with NPDR have greater MG dropout and meiboscore, as well as more severe MGD and DED parameters than persons with NDM.

Navigating the transition: a multidisciplinary approach to inflammatory bowel disease in children.

PURPOSE: A multidisciplinary approach to Inflammatory Bowel Disease (IBD) has recently demonstrated a positive impact in pediatric patients, reducing dropout rates and facilitating the transition to adult care. Our study aims to evaluate how this approach influences disease activity, dropout rates, and transition. METHODS: We conducted a longitudinal observational study including all patients diagnosed with IBD during pediatric-adolescent age, with a minimum follow-up period of 12 months. For each patient, endpoints included therapeutic approach, need for surgery and transition features. RESULTS: We included 19 patients: 13 with Ulcerative Colitis (UC) and 6 with Crohn's disease (CD). Most patients required multiple lines of therapy, with over 50% in both groups receiving biological drugs. Compliance was good, with a single dropout in each group (10, 5%). The need for surgery was significantly higher in the CD group compared to the UC group (16% vs. 7.7%, p < 0.01). Mean age at transition was significantly higher in the UC group compared to the CD group (19.2 ± 0.7 years SD vs. 18.3 ± 0.6 years SD, p < 0.05). CONCLUSIONS: In our experience, the multidisciplinary approach to IBD in transition-age patients appears effective in achieving clinical remission, offering the potential to reduce therapeutic dropouts.

Correlates of burnout and dropout intentions in medical students: A cross-sectional study.

BACKGROUND: Burnout is a pervasive issue among medical students, exhibiting a high prevalence that jeopardizes their academic success and may also predispose them to more severe affective disorders such as depression. This study aims to explore the complex relationships between psychological capital (PsyCap), general social support, educational satisfaction, and burnout, and how these factors collectively influence dropout intentions. METHODS: A non-probabilistic convenience sample was collected through an online survey from first- and second-year medical students at a Faculty of Medicine in Portugal. The survey employed psychometric instruments to measure burnout (BAT-12), social support (F-SozU K-6), PsyCap (CPC-12R), satisfaction with education, and dropout intentions (Screening Instrument for Students At-Risk of Dropping Out). Structural equation modeling was applied to analyze the data from 351 participants. RESULTS: The model demonstrated a significant positive association between burnout and dropout intentions (ß̂ = 0.37; p < 0.001), underscoring burnout as a direct correlate of dropout intentions alongside educational satisfaction (ß̂ = -0.25; p = 0.003) and PsyCap (ß̂ = -0.22; p = 0.005). Higher social support is associated with reduced burnout (ß̂ = -0.28; p < 0.001) and increased educational satisfaction (ß̂ = 0.22; p = 0.002). LIMITATIONS: The non-probabilistic sampling method prevents the generalization of the findings. The cross-sectional data do not permit the inference of temporal relationships between the studied variables. CONCLUSIONS: These findings emphasize the importance that burnout may have on dropout intentions, and contribute to the understanding of affective syndromes such as burnout in educational settings.

Determining factors of sports dropout of young scholars: a cross-sectional analysis in the 8-13 year age group.

This article investigates the phenomenon of sports abandonment among young scholars aged between 8 and 13 years. Regardless of the growing awareness of the importance of sport and physical activity during childhood and adolescence, this theme must be adequately explored in the scientific literature. Our study addresses this gap through a cross-sectional research design, tracking and analyzing data from a cohort of young athletes over one year. The main objective of our study is to identify the determinants leading to sports dropout in this specific age group. We looked at several possible causes through a multivariate analysis, including social pressures, parental expectations, time conflicts, physical and psychological stress, and lack of enjoyment. The results show a significant attrition rate, with psychosocial factors emerging as the most influential in determining whether a young person will continue or stop their participation in sport. Furthermore, our study highlights the importance of targeted interventions and preventive strategies that promote a positive, inclusive, and balanced sports environment for adolescents. These interventions can be particularly effective when implemented by coaches, parents and others involved in youth sports education. Finally, this paper discusses the implications of the findings for sports professionals, physical educators, and public policy makers. It highlights the need for more effective support policies and innovative pedagogical approaches to promote sporting persistence during adolescence. Our findings can serve as a starting point for further research in this field, helping to build a future where young people can enjoy the many benefits of sport and physical activity.

Establishment and validation of a nomogram for dropout intention in Chinese early year medical undergraduates.

BACKGROUND: The attrition rate of Chinese medical students is high. This study utilizes a nomogram technique to develop a predictive model for dropout intention among Chinese medical undergraduates based on 19 individual and work-related characteristics. METHOD: A repeated cross-sectional study was conducted, enrolling 3536 medical undergraduates in T1 (August 2020-April 2021) and 969 participants in T2 (October 2022) through snowball sampling. Demographics (age, sex, study phase, income, relationship status, history of mental illness) and mental health factors (including depression, anxiety, stress, burnout, alcohol use disorder, sleepiness, quality of life, fatigue, history of suicidal attempts (SA), and somatic symptoms), as well as work-related variables (career choice regret and reasons, workplace violence experience, and overall satisfaction with the Chinese healthcare environment), were gathered via questionnaires. Data from T1 was split into a training cohort and an internal validation cohort, while T2 data served as an external validation cohort. The nomogram's performance was evaluated for discrimination, calibration, clinical applicability, and generalization using receiver operating characteristic curves (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA). RESULT: From 19 individual and work-related factors, five were identified as significant predictors for the construction of the nomogram: history of SA, career choice regret, experience of workplace violence, depressive symptoms, and burnout. The AUC values for the training, internal validation, and external validation cohorts were 0.762, 0.761, and 0.817, respectively. The nomogram demonstrated reliable prediction and discrimination, with adequate calibration and generalization across both the training and validation cohorts. CONCLUSION: This nomogram exhibits reasonable accuracy in foreseeing dropout intentions among Chinese medical undergraduates. It could guide colleges, hospitals, and policymakers in pinpointing students at risk, thus informing targeted interventions. Addressing underlying factors such as depressive symptoms, burnout, career choice regret, and workplace violence may help reduce the attrition of medical undergraduates. TRIAL REGISTRATION: This is an observational study. There is no Clinical Trial Number associated with this manuscript.

Who stays? Who goes? Motivation and tendency to drop out in music schools.

Based on self-determination theory, this study examined the extent to which the satisfaction of the basic psychological needs for autonomy, competence, and social relatedness in instrumental lessons explain the quality and quantity of motivation, which are responsible for persistence and dropout in music schools. This study also investigated whether parental involvement contributes to dropout. A total of 140 music students from Austria (37.16% male, 62.1% female, 0.8% diverse) were surveyed using a quantitative questionnaire. The central variables are the tendency to dropout (dependent variable) and, as predictors, the motivational regulation styles, the satisfaction of basic psychological needs in the classroom and parental involvement. The results of a structural equation model indicated that satisfaction of basic needs in class and parental involvement, mediated by motivation, predicted dropout tendencies. Autonomous motivation in lessons is negatively associated and controlled motivation is positively associated with the tendency to drop out of music schools. Satisfaction of basic psychological needs during lessons and parental involvement predicts autonomous motivation. However, basic psychological needs cannot predict controlled motivation but parental involvement can predict controlled motivation to a limited extent. Finally, this study emphasizes the practical importance of need satisfaction and parental involvement in motivation and continuing to play a musical instrument.

Rethinking treatment failures. Research on a group of Italian psychotherapists.

Introduction: Psychotherapeutic failures involve situational, relational, and personal factors. Dropout refers to a patient's unilateral termination of treatment without the therapist's knowledge or approval. Premature termination occurs when therapy is discontinued before achieving a sufficient reduction in initial problems. Objective: This study explores the role of therapist's emotional response (countertransference), gender, psychotherapeutic orientation, and patient diagnosis in the context of psychotherapeutic failures. Method: A mixed-method approach was used. Fifty-nine Italian psychotherapists, practicing mostly privately with at least 5 years of experience, were recruited through Italian professional internet websites. The Therapist Response Questionnaire and the Impasse Interview were administered to each psychotherapist. Each therapist was asked to reflect on their last dropout patient. Quantitative (MANOVA) and qualitative analyses (textual content analysis) were conducted with SPSS and T-LAB, respectively. Results: The quantitative analyses revealed that the most frequent countertransference response was Helpless/Inadequate, with female therapists experiencing this more frequently than male therapists. The qualitative analyses identified two main factors explaining most of the variance in countertransference responses: Parental/Protective versus Hostile/Angry, and Positive/Satisfying versus Helpless/Inadequate, with Helpless/Inadequate central. Additionally, the qualitative analysis of treatment interruption methods revealed two factors explaining over 50% of the variance. Lack of communication was linked to negative themes, while mediated and direct communication were associated with positive terms. Direct communication was characterized as useful, while mediated communication was linked to dropout and attachment figures. Conclusion: Under pressure, psychotherapists' anxiety levels increase, often managed ambivalently or avoidantly. These results suggest that awareness of psychotherapist emotional responses is important to limit psychotherapeutic failures. These findings offer valuable insights for clinical practice.

Drop-out rates in animal-assisted psychotherapy - Results of a quantitative meta-analysis.

INTRODUCTION: Animal-assisted psychotherapy is an emerging field with great potential and growing popularity. However, empirical research on its effectiveness is insufficient, and consistent evidence about patients' commitment is missing. The present meta-analysis addresses this gap by systematically comparing drop-out rates in animal-assisted psychotherapy and by relating the resulting across study drop-out rate to across study drop-out rates reported in meta-analyses on conventional psychotherapy. METHOD: Fifty-seven studies published until August 2022 were identified as eligible for meta-analytic comparison, that is, they conducted animal-assisted psychotherapy on at least one group of psychiatric patients and reported drop-out rates. Potential moderating influences of the type of animal and patients' disorder were considered, as well as multiple other demographic and study design variables. RESULTS: The across study drop-out rate in animal-assisted psychotherapy was 11.2%. This was significantly lower than the across meta-analyses drop-out rate of conventional psychotherapy (d = -.45, p = .0005). Although effects of moderator variables could not be evaluated statistically due to too small and heterogeneous data sets, descriptive results suggest influences of the type of animal and patient disorder. However, study quality ratings identified serious shortcomings regarding proper research design, most critically concerning the report of effect size measures, the use of standardized intervention plans and Open Science practices. CONCLUSION: Drop-out constitutes a major problem of psychotherapeutic research and practice. By proposing that the inclusion of an animal in the psychotherapeutic setting can enhance patients' commitment and by outlining challenges and opportunity of animal-assisted psychotherapy, this meta-analysis offers a starting point for future research in this evolving field.

The associations between organized sport participation and physical fitness and weight status development during adolescence.

OBJECTIVES: We are yet to understand how continuous participation in organized sports, dropout from organized sports, or complete non-participation affect adolescents' trajectories of physical fitness and body mass index (BMI). Thus, the aim was to examine longitudinal changes in cardiorespiratory and muscular fitness, and BMI between adolescents 1) who continued or started organized sport participation, 2) who dropped out, and 3) who never participated in organized sport or dropped out before adolescence. DESIGN: Longitudinal observational study. METHODS: Over four years (2017-2021), sport participation, cardiorespiratory and muscular fitness, and BMI data were collected annually from 963 participants (Mage = 11.25 ±â€¯0.31). Latent growth curve models were utilized to examine levels (baseline) and slopes (rate of change) of BMI, cardiorespiratory, and muscular fitness in each sport participation group. RESULTS: Fitness levels significantly varied among groups. Continuing sport participants exhibited the highest levels, non-participants the lowest. Both groups showed significant improvements in cardiorespiratory and muscular fitness over time. Dropouts had higher baseline fitness than non-participants but demonstrated no change in cardiorespiratory fitness over time and a significantly smaller increase in muscular fitness than the two other groups. BMI increased similarly in all groups, with non-participants starting at higher baseline levels. CONCLUSIONS: Individuals who continually participated in sports maintained higher levels of fitness than individuals who did not participate in organized sports across adolescence. However, individuals who dropped out of organized sports, showed plateau in their fitness improvements, suggesting that the physical activity previously obtained through organized sports may not be replaced elsewhere.

Visual predictive check of longitudinal models and dropout.

Visual predictive checks (VPC) are commonly used to evaluate pharmacometrics models. However their performance may be hampered if patients with worse outcomes drop out earlier, as often occurs in clinical trials, especially in oncology. While methods accounting for dropouts have appeared in literature, they vary in assumptions, flexibility, and performance, and the differences between them are not widely understood. This manuscript aims to elucidate which methods can be used to handle VPC with dropout and when, along with a more informative VPC approach using confidence intervals. Additionally, we propose constructing the confidence interval based on the observed data instead of the simulated data. The theoretical framework for incorporating dropout in VPCs is developed and applied to propose two approaches: full and conditional. The full approach is implemented using a parametric time-to-event model, while the conditional approach is implemented using both parametric and Cox proportional-hazard (CPH) models. The practical performances of these approaches are illustrated with an application to the tumor growth dynamics (TGD) modeling of data from two cancer clinical trials of nivolumab and docetaxel, where patients were followed until disease progression. The dataset consisted of 3504 tumor size measurements from 855 subjects, which were described by a TGD model. The dropout of subjects was described by a Weibull or CPH model. Simulated datasets were also used to further illustrate the properties of the VPC methods. The results showed that the more familiar full approach might not provide meaningful improvement for TGD model evaluation over the naive approach of not adjusting for dropout, and could be outperformed by the conditional approach using either the Weibull model or the Cox proportional hazard model. Overall, including confidence intervals in VPC should improve interpretation, the conditional approach was shown to be more generally applicable when dropout occurs, and the nonparametric approach could provide additional robustness.

Medical Professionalism and Its Association with Dropout Intention in Peruvian Medical Students during the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic introduced unprecedented challenges to medical education systems and medical students worldwide, making it necessary to adapt teaching to a remote methodology during the academic year 2020-2021. The aim of this study was to characterize the association between medical professionalism and dropout intention during the pandemic in Peruvian medical schools. METHODS: A cross-sectional online-survey-based study was performed in four Peruvian medical schools (two public) during the academic year 2020-2021. Medical students, attending classes from home, answered three scales measuring clinical empathy, teamwork, and lifelong learning abilities (three elements of medical professionalism) and four scales measuring loneliness, anxiety, depression, and subjective wellbeing. In addition, 15 demographic, epidemiological, and academic variables (including dropout intention) were collected. Variables were assessed using multiple logistic regression analysis. RESULTS: The study sample was composed of 1107 students (390 male). Eight variables were included in an explanatory model (Nagelkerke-R2 = 0.35). Anxiety, depression, intention to work in the private sector, and teamwork abilities showed positive associations with dropout intention while learning abilities, subjective wellbeing, studying in a public medical school, and acquiring a better perception of medicine during the pandemic showed a negative association with dropout intention. No association was observed for empathy. CONCLUSIONS: Each element measured showed a different role, providing new clues on the influence that medical professionalism had on dropout intention during the pandemic. This information can be useful for medical educators to have a better understanding of the influence that professionalism plays in dropout intention.