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BackgroundElectronic health records (EHRs) are a cost-effective approach to provide the necessary foundations for clinical trial research. The ability to use EHRs in real-world clinical settings allows for pragmatic approaches to intervention studies with the emerging adult HIV population within these settings; however, the regulatory components related to the use of EHR data in multisite clinical trials poses unique challenges that researchers may find themselves unprepared to address, which may result in delays in study implementation and adversely impact study timelines, and risk noncompliance with established guidance.ObjectiveAs part of the larger Adolescent Trials Network (ATN) for HIV/AIDS Interventions Protocol 162b (ATN 162b) study that evaluated clinical-level outcomes of an intervention including HIV treatment and pre-exposure prophylaxis services to improve retention within the emerging adult HIV population, the objective of this study is to highlight the regulatory process and challenges in the implementation of a multisite pragmatic trial using EHRs to assist future researchers conducting similar studies in navigating the often time-consuming regulatory process and ensure compliance with adherence to study timelines and compliance with institutional and sponsor guidelines.MethodsEight sites were engaged in research activities, with 4 sites selected from participant recruitment venues as part of the ATN, who participated in the intervention and data extraction activities, and an additional 4 sites were engaged in data management and analysis. The ATN 162b protocol team worked with site personnel to establish the necessary regulatory infrastructure to collect EHR data to evaluate retention in care and viral suppression, as well as para-data on the intervention component to assess the feasibility and acceptability of the mobile health intervention. Methods to develop this infrastructure included site-specific training activities and the development of both institutional reliance and data use agreements.ResultsDue to variations in site-specific activities, and the associated regulatory implications, the study team used a phased approach with the data extraction sites as phase 1 and intervention sites as phase 2. This phased approach was intended to address the unique regulatory needs of all participating sites to ensure that all sites were properly onboarded and all regulatory components were in place. Across all sites, the regulatory process spanned 6 months for the 4 data extraction and intervention sites, and up to 10 months for the data management and analysis sites.ConclusionsThe process for engaging in multisite clinical trial studies using EHR data is a multistep, collaborative effort that requires proper advanced planning from the proposal stage to adequately implement the necessary training and infrastructure. Planning, training, and understanding the various regulatory aspects, including the necessity of data use agreements, reliance agreements, external institutional review board review, and engagement with clinical sites, are foremost considerations to ensure successful implementation and adherence to pragmatic trial timelines and outcomes.

Research on student selection mostly focuses on accepted applicants and the effects of selection procedures. In this sense, most samples seem to be biased, which is well-reflected in the literature. The present study investigates student selection regarding students who had been initially de-selected but finally succeeded in the admission process. Our results show that students from the de-selected group reveal lower performance, i.e. higher dropout, and thus lead to decreases in relative organisational performance. We argue that higher education institutions may be confronted with a performance dilemma if external stakeholders prefer institutional growth, and internal actors prefer maintenance of educational standards. In this situation, reduced graduation rates have the potential to undermine the output legitimacy of higher education institutions. Therefore, we recommend focusing more on the balance between academic standards, stakeholders' demands, and processes of student selection. From a theoretical point of view, we suggest broadening the perspective and combining selection theory with organisation theory. [ABSTRACT FROM AUTHOR]

Male veterans are vastly over-represented in suicide rates relative to non-veterans. A critical avenue for improving male veterans' mental health outcomes is improving their engagement with mental health services. This study presents a qualitative investigation of mental health practitioners' perspectives on enhancers of engagement in, and drivers of dropout from therapy among male veterans. Participants were 138 mental health practitioners across Australia, the USA, Canada, New Zealand and the UK (44.9% male; age M = 47.5 years, SD = 12 years). Participants responded to qualitative survey items inquiring about their perspectives on what works to engage male veterans in therapy, alongside common drivers of therapy dropout. Under an overarching theme contextualising the therapeutic alliance between veterans and mental health practitioners, interpretive description analyses led to eight distinct subthemes. Results highlight the range of areas in which mental health practitioners can thoughtfully adapt their practice to engage male veterans and align with military masculinities. In addition, findings underscore the range of barriers facing veterans when they seek help, which can precipitate dropout if not overcome by the right balance between practitioner engagement and veteran persistence. [ABSTRACT FROM AUTHOR]

Student dropout represents a significant challenge in distance higher education. To better understand this issue, a comprehensive analysis of institutional data, spanning several years from a private German distance learning university of applied sciences, was conducted. The primary objectives were twofold: (1) to pinpoint institutional factors serving as predictors for student dropout and (2) to analyze the underlying psychological mechanisms. The findings indicate that part-time enrollment, age, interruptions, and overdue payments predicted dropout. Conversely, a good match between a student's occupation and the study program, as well as employer reimbursement of study fees, predicted degree completion. Further results suggest that students who recommend the program to others are more likely to succeed. However, those referred by friends are at a higher risk of dropping out. Additionally, poor grades and late submission of the first assignment were identified as predictors of dropout. A noteworthy finding was the interaction between these factors and the student's qualification for studying. Vocationally qualified students tend to submit their first assignment earlier but perform worse academically compared to academically qualified students. Generally, the influence of socio-demographic factors such as the educational background, gender, or nationality was low. This suggests that some of the disadvantages that non-traditional students might face at traditional universities in Germany might cease to exist at private distance universities of applied sciences. The implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

Objective: Dropout from psychotherapy remains an issue across various treatment modalities and psychological disorders, with roughly 20% of clients failing to complete treatment. Dropping out of psychotherapy is associated with worse psychological and physical health outcomes. This study aimed to use a clinically generalisable definition of dropout to identify risk factors for dropping out of psychotherapy. Method: Retrospective chart review methods were used to collect data on 203 clients seen at a community‐based clinical psychology doctoral training clinic. Results: Independent samples t‐tests and chi‐squared tests for independence indicated that clients who dropped out of psychotherapy were more likely to be non‐students, live farther away from the treatment clinic and no‐show at least once in the first four sessions of therapy. Discussion: To address dropout risk, it is important that clinicians be attuned to possible indicators of structural and motivational barriers to treatment engagement. [ABSTRACT FROM AUTHOR]

To investigate the prevalence and moderators of dropout rates among adults with knee or hip osteoarthritis participating in exercise randomized controlled trials (RCTs). Two authors searched Embase, CINAHL, PsycARTICLES, and PubMed up to 01/09/2023. We included RCTs of exercise interventions in people with knee or hip osteoarthritis that reported dropout rates. Dropout rates from exercise and control conditions and exerciser/participant, provider, and design/implementation related moderators. In total, 209 RCTs involving 277 exercise arms in 13,102 participants were included (mean age at study level=64 years; median prevalence of men participants=26.8%). The trim-and-fill-adjusted prevalence of dropout across all RCTs was 17.5% (95% CI=16.7%-18.2%), which is comparable with dropout observed in control conditions (trim-and-fill-adjusted odds ratio=0.89; 95% CI=0.71-1.12, P =.37). Higher prevalence of antidepressant use at study-level predicted higher dropout (R2=0.75, P =.002, N RCTs=6, n exercisers=412). Supervision by an exercise professional was associated with lower dropout rates, with a trim-and-fill-adjusted rate of 13.2% (95% CI=11.7%-14.9%) compared with 20.8% without supervision (95% CI=18.3%-23.5%) (P <.001). Dropout rates for exercise in RCTs are comparable with control conditions, suggesting that exercise is a generally well-accepted intervention. However, interventions should be supervised by an exercise professional, such as a physiotherapist or exercise physiologist, to further minimize the risk of dropout. Health professionals should consider participants' use of antidepressants as a risk factor for dropout from exercise. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Research on teachers’ relational practices and how they develop relationships with their students is a growing field, internationally and in Scandinavia. However, research on teachers’ relational work in disadvantaged areas is rare. This study examines how vocational teachers in schools in disadvantaged areas describe their relational work with students. Taking the narratives of 25 vocational students about their teachers as a starting point, the study is based on individual in-depth interviews with the students’ contact teachers (N = 8). Reflexive thematic analysis revealed several key aspects integral to teachers’ relational work. The findings suggest that building positive relationships with students entails a balancing act between providing close support and fostering students’ autonomy. Furthermore, building relationships also requires a balance between recognising individual needs and developing strong classroom communities. Finally, teachers’ relational work with students can be described as an act of caring and subtle communication. Implications for practice are discussed. [ABSTRACT FROM AUTHOR]

The majority of higher education institutions are concerned with keeping students enrolled until they graduate. This article aims to explore why engineering students do not complete their degree studies and drop out their studies without completion. The sample consisted of 275 students enrolled in a faculty of engineering. To interpret the data, sociological concepts of structure and agency were utilized. This study outlines 12 factors that influence the decision to drop out of course by a student majoring in engineering. A decision-making trial and evaluation laboratory (DEMATEL) method used to analyze the result. Further this DEMATEL method merged with fuzzy and intuitionistic fuzzy sets and it is observed that intuitionistic fuzzy reduce the randomness and uncertainty in expert judgment and produce better result than DEMATEL and fuzzy DEMATEL. Amongst the twelve factors, the financial and domestic constraints came out to be the most influential one that drives a student to leave engineering studies with a score of 2.45, while lack of enjoyment/interest appeared as the least influential effect with a score of -0.91. [ABSTRACT FROM AUTHOR]

Introduction: The intention to dropout and dropout is a problem still unresolved in higher education institutions. Objective: To estimate the differences in the levels of engagement, motivation and academic satisfaction according to (a) intention to dropout and (b) students who remained with those who dropped out. Method: non-experimental designs were used. Two studies are reported, study 1 involved 3,256 students and study 2 involved 2,110 students. The Utrecht Work Engagement Scale Student Test, the Academic Self-Regulation Scale and the Academic Satisfaction Scale were used. The intention to dropout was measured with 3 items and the final dropout data was taken from the official register of students who dropped out of university. Results: There are significant differences in the levels of engagement, autonomous motivation and satisfaction between the students who remained and those who dropped out of the university. Discussion: Students who dropped out in the 3rd semester presented lower levels of academic engagement, motivation and academic satisfaction than those who remained. The intention to dropout and lower levels of these cognitive-motivational variables may contribute to the identification of students at high risk of dropping out. These results contribute to unveiling key variables for the educational transformation of Higher Education in the 21st century. [ABSTRACT FROM AUTHOR]

Introduction: Conduct disorder (CD) is a severe mental disorder in youth. Yet, providing psychological interventions for adolescents with CD is challenging. This patient group is often characterized by risk factors for therapy dropout such as, e.g., CD symptoms and being in middle adolescence. On the other hand, little is known about characteristics of adolescents with CD who complete treatment. To gain more insight into what might become a successful therapy with adolescents with CD, this study explores baseline characteristics and drop-out occurrence in patients with CD referred to mentalization-based treatment for adolescents with CD (MBT-CD). More specifically, this study aims at identifying clusters of adolescent patients based on age, CD symptom severity and personality pathology at the beginning of treatment which may have come along with a higher or lower dropout occurrence. Methods: Following implications of an elbow plot, a 3-means cluster-analysis was conducted on self-report baseline data of N = 32 adolescents with CD (n = 11 dropouts, n = 21 completers) who participated in a feasibility study on MBT-CD. Additionally, in an exploratory stepwise logistic regression, variables were explored as potential predictors of dropout. Results: Cluster 1 consisted of n = 14 adolescents, of whom n = 8 (57%) dropped out. Cluster 2 consisted of n = 5 adolescents of whom 1 (20%) dropped out. Cluster 3 consisted of N = 13 adolescents, of whom only n = 2 (15%) dropped out. Cluster 2 showed descriptively the highest CD symptom severity. While adolescents in Clusters 1 and 3 showed in part similarities in CD symptom severity, personality pathology was descriptively markedly higher in Cluster 1. In the stepwise logistic regression, only intimacy personality pathology was identified as potential predictor for dropout. Discussion: This study's exploratory findings point to different types of adolescents with CD coming along with different chances for therapists to conduct a (successful) psychotherapy. Herein, low personality functioning in the intimacy domain, rather than CD symptoms as aggressiveness, may play a crucial role. While findings are limited by the small sample size, they may be able to shed increasing light on conducting (successful) psychotherapy with a scarcely researched patient group. [ABSTRACT FROM AUTHOR]

Background: The abandonment of psychotherapeutic treatments is influenced by various factors, including patient characteristics, therapist traits, and the therapeutic relationship. Despite the well-documented importance of these factors, limited empirical research has focused on the role of the therapeutic relationship and the characteristics of therapist-patient dyads in predicting treatment dropout. This study protocol outlines a longitudinal research project aimed at predicting dropout and non-response in psychotherapy for individuals with personality disorders. The research seeks to identify predictive factors related to psychotherapy outcomes, focusing on patient, therapist, and dyadic elements. Specifically, the study will examine the influence of therapist characteristics (e.g., personality traits, countertransference, responsiveness) on treatment outcomes, explore the impact of relational factors (e.g., treatment expectations, epistemic trust, therapeutic alliance) on therapy effectiveness, and assess how the therapeutic alliance within therapist-patient dyads affects the likelihood of dropout and non-response. Methods: The longitudinal study will include 100 therapist-patient dyads (200 participants) recruited from various Mental Health Services in Milan, Italy. Validated instruments will be administered to both patients and therapists at four-time points: T0 (baseline), T1 (3 months), T2 (6 months), and T3 (1 year). Data will be collected at baseline and at the one-year mark to evaluate the relationships between therapist, patient, and dyadic factors and treatment outcomes. Discussion: Identifying predictive variables associated with high dropout rates can help preempt treatment discontinuation, reducing the financial and operational burdens on mental health services. Understanding these factors will enable the development of targeted interventions to improve treatment engagement and reduce attrition. This approach could enhance outcomes for individuals with personality disorders and lead to more efficient resource allocation and sustainable delivery of mental health care. [ABSTRACT FROM AUTHOR]

Students are termed "multitaskers," and it is likely that they easily fall prey to other subjects or topics that most interest them. They occasionally took heed or gave close and thoughtful attention to the lectures they were on. In the current educational system, our young generations receive materials from their leftovers, and their constant behavioral classification has decided the material to learn. The rate at which many students gave up on their studies was predominantly higher in online classroom than in offline classroom due to the lack of direct interaction between the students and teachers. To eradicate this and to make online classroom an effective one, the proposed model can be put forth in each class to predict the student's behavior based on their keen interests. The model predicts and recommends their live session-wise apt course materials to learn. This model uses machine learning generic algorithms and the chi-square test to analyze their manners. The intelligent Online Poll Bot (OPB) acts as a teacher in this virtual learning environment by engaging in live interactions during class time. It is developed using GAN and the IBM Watson Framework. This paper analyzes the time complexity and accuracy of the developed poll bot, and 96.82% accuracy was achieved with the proposed GAN-based poll bot. Students can be categorized according to their learning behavior by using the Optimal Behavior Prediction Cluster (OBPC). These OBPCs will let know the number of clusters at the beginning of the process itself. According to the model, the study materials are preferred based on the students' performance in each class. In online learning environments, the Live Behavior Analysis (LBA) method using the proposed OBPC and OPB can create interactive learning environments and deliver behavior-based study materials to learners, thus reducing dropout rates. The proposed experiments show that the accuracy of the OBPC-based system is 97.43%, and LBA produces 96.52% accuracy, 95.13% F-Score, 97.13% recall, and 96.14% precision compared to existing approaches. This technology will reduce the number of dropouts and can effectively predict the behavior of all students in the virtual environment where they are placed. [ABSTRACT FROM AUTHOR]

Objectives: Although treatments of patients with borderline personality disorder (BPD) were historically associated with relatively high dropout rates, dropout rates in contemporary evidence‐based treatments for BPD are typically substantially lower. However, only a few studies have investigated dropout rates in mentalization‐based treatment (MBT), and even fewer have investigated predictors of dropout in this type of treatment. In this study, we investigated dropout rates in two types of MBT (day hospital MBT [MBT‐DH] and intensive outpatient MBT [MBT‐IOP]) using data from a recent multicenter randomized clinical trial. Given the central importance of attachment considerations in MBT, we also investigated the relationship between dropout in these two treatments and attachment dimensions. Design: Within a multicenter randomized clinical trial, 114 BPD patients were randomized to MBT‐DH (n = 70) or MBT‐IOP (n = 44). Methods: Dropout in both types of MBT was investigated using descriptive analyses, and its association with attachment anxiety and attachment avoidance, as measured by the Experiences in Close Relationships questionnaire at baseline, was investigated using regression analyses. Results: Dropout rates were relatively low (10.5% across both types of MBT) and did not significantly differ between groups (11.4% in MBT‐DH, 9.1% in MBT‐IOP). Attachment avoidance and attachment anxiety did not impact dropout, nor did their interaction or the interaction with the type of MBT. Conclusions: Low dropout rates in both types of MBT indicate a high level of engagement of patients in both programmes. Attachment dimensions were not associated with dropout, consistent with the principle that MBT is tailored to each individual's needs. More research is needed, however, to investigate to what extent attachment is a dynamic context‐bound adaptive process rather than a static personality feature. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many existing student persistence and retention studies overlooked the different types of departure (e.g., dropout, transfer) and aggregate them into a single departure variable, as a result, the estimates from these studies are spurious when one is interested in what student characteristics prompt dropout but not transfer. This can be especially relevant when studying the departure behavior of specific student subgroups, such as first-generation students. In response to this shortcoming in previous studies, this study longitudinally analyzed first-generation students' dropout and transfer behaviors. Paired with nationally represented data, this study uncovered how various student characteristics impact dropout and transfer behaviors differently over time. First-generation students were more likely than students with college-educated parents to leave the system than to transfer. A higher sense of belonging was found to reduce the likelihood of students transferring to other institutions academic years one through four. [ABSTRACT FROM AUTHOR]

Youth sport participation, retention, and dropout are well-established areas of academic interest in the interdisciplinary field of sport. However, terminology, definitions, and conceptualisations of participation vary significantly within the literature. This variation can potentially lead to inadvertent misreporting or deliberate manipulation of membership and/or participation data by clubs and sporting organisations, particularly when applying for highly prized, competitive grant funding opportunities. This paper advances sport participation terminology by reconceptualising youth sport behaviours that can be situated within the phases of enrolment and entry, retention, sustained engagement, sport transfer, dropout, sustained disengagement, and critically, re-engagement. The purpose of this critical commentary is to highlight the potential benefits of purposefully redefining phases of 'sport participation' and propose the Participation Re-Engagement (PRE) model – a new conceptualisation of sport participation for researchers, policymakers, and practitioners to enhance policies, programmes, and interventions aimed at increasing organised youth sport participation. [ABSTRACT FROM AUTHOR]

Despite growing interest in interventions aimed at enhancing emotional well-being, little research has addressed the question of engagement. This study explored engagement in a 7-day online well-being intervention study involving 24,180 participants from 195 countries/territories (78% female, Mage = 49, 62% White). Following an onboarding survey, participants completed a morning well-being practice and an evening follow-up survey for the week. Overall, 76% of the participants initiated the intervention (i.e. returned to the platform after enrollment to start the intervention), completing an average of four daily practices. Several demographic (e.g. being older, White) and psychological variables (e.g. lower financial strain, higher life satisfaction) emerged as common predictors of initiating the intervention and completing more practices. Age was a particularly important predictor across outcomes. These findings offer novel insights into how individual characteristics relate to engagement in online well-being interventions and have implications for both designing the interventions and interpreting findings. [ABSTRACT FROM AUTHOR]

Taking the perspective of career choice as a lifelong, iterative, constructive, and agentic process, the present study focuses on the development of vocational aspirations of nursing trainees; that is, thoughts about a long-term perspective in nursing (i.e., persistence), ideas of finishing the training but changing into another profession after some time (i.e., withdrawal), and decisions to terminate the training before completing the programme through a final examination (i.e., dropout). In order to generate detailed insights about the dynamics behind the development of such aspirations during the initial training phase, a qualitative, longitudinal, within-subject study design based on grounded theory was employed. The results mainly show that social interactions with more experienced nurses, practical work experiences, encounters with environments that are either conducive to learning or not, the satisfaction of different needs (e.g., autonomy, competence, belonging, sense of meaningfulness), as well as the associated feelings of well-being affect how vocational aspirations develop over the first year of training. In addition, the study identifies four different patterns of how trainees typically oscillate between thoughts of staying in nursing and leaving the profession in the short or long run: (a) arriving and wanting to stay, (b) staying as a transitional passage, (c) seeking to stay, and (d) exiting as a knee-jerk reaction. The patterns present evidence of a variety of approaches regarding how trainees deal with certain experiences during their training and how the combination of experiences might affect young professionals' subsequent career choices. [ABSTRACT FROM AUTHOR]

In 2022, one in four vocational education and training (VET) contracts resulted in premature termination—a potential sign of inefficiency in the training of urgently needed skilled professionals. This study focuses on the level of perceived informedness of trainees and its potential influence on dropout. This includes trainees' perceived level of information about the characteristics and requirements of an occupation, which may result from various sources of information in the form of social contacts. We draw on theories from the field of career choice and job search to argue that a better level of informedness can reduce uncertainty and enable individuals to make better-informed decisions about their careers. With better informedness, there is a higher probability of finding a suitable match between a trainee and their chosen occupation, ultimately increasing their likelihood of job persistence. This should also lead to a lower probability of dropout among VETs. Using data from the National Educational Panel Study, starting cohort 4, we employed event history analysis. We show that good perceived informedness on the requirements of one's vocational training and the perception of good school-based vocational preparation reduce the probability of dropping out. In contrast, good informedness about alternative training programs increases dropout risk. We also see that the distinction between closer or more distant groups of people from the social network (strong or weak ties) who provide information about training is not decisive. [ABSTRACT FROM AUTHOR]

Introduction: In the fields of labor market and education research, there is a vast interest in mental health factors affecting unsuccessful school‐to‐work transitions, dropout from school and labor market disconnections for young people. Young people who are not in employment, education or training are conceived of as NEET. Aim: To get an overview we conducted a systematic review of the present literature on the influence of mental health on the likelihood of becoming NEET in Europe. Method: A Systematic literature search was conducted in four databases on February 21, 2023, with an update on January 15, 2024. Results: 33,314 articles were screened whereas 41 studies involving 8,914,123 individuals were included. Poor mental health such as attention deficit hyperactive disorder, autism, depression, borderline, and psychosis during childhood and adolescence is strongly associated with becoming NEET. Conclusion: Mental health issues, whether mild or severe, heighten significant the risk of adverse education and employment outcomes in early adulthood, extending to young individuals with personality disorders, borderline personality disorder, and psychoses. These observations inform early intervention strategies for children and young people grappling with mental health challenges. Timely treatment is essential. Future research should focus on the gap in research like specific disorders such as eating disorders and anxiety. [ABSTRACT FROM AUTHOR]

Most education systems have set a minimum age until which students must stay at school. In the case of Spain, students can drop out the same day they reach that age, even without finishing that academic year. In the present research work, we intend to analyse the influence of early dropout on later life outcomes for the Spanish population, i.e. literacy and numerical skills and earnings. In order to do this, we employ PIAAC 2012 data and a fuzzy regression discontinuity design, focusing on a transition period between education laws: a first period without minimum dropout age and a second period (law approved in 1970) which obligated students to be at least 14 years-old to dropout. Our results show, for this particular cohort of the Spanish population, that dropping out is substantially negatively associated with literacy and numerical skills in around 1.5 standard deviations, while it did not influence future earnings. [ABSTRACT FROM AUTHOR]

Dropout in perpetrator intervention programs (PIPs) is extensively documented in the literature, yet findings across various studies exhibit global inconsistency. This meta-analysis aimed to identify dropout rates among individuals attending PIPs and examine sociodemographic, violence-related, intrapersonal, interpersonal, and external variables related to dropout. A search was conducted across six databases, encompassing studies published between 2010 and 2022 in English, Spanish, or Portuguese. Thirty manuscripts, comprising data from 31 independent samples, were included. Dropout rates varied from 9% to 67%. Bivariate analysis results indicated that younger age (OR = 0.69), non-White ethnicity (OR = 1.54), unemployment (OR = 1.78), offender typology other than family only (OR = 2.45), substance abuse (OR = 1.78), presenting a personality disorder (OR = 1.21), engaging in problematic leisure activities (OR = 1.28), possessing a greater criminal history (OR = 1.47), and experiencing more adversity in childhood (OR = 1.44) were significantly correlated with dropout. Additionally, the inclusion of motivational strategies in treatment (OR = 0.44) significantly decreased the likelihood of dropout. Results from multivariate analyses revealed that younger age (OR = 0.63), presenting a personality disorder (OR = 1.73), and experiencing more adversity in childhood (OR = 2.16) were significantly associated with dropout. Notably, intimate partner violence characteristics established a significant negative relation with dropout (OR = 0.59). Findings indicate that variables associated with dropout align with those related to general and intimate partner violence recidivism, suggesting that individuals requiring more intensive intervention are those who derive less benefit from it. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine research on school dropout in open and distance education in the Web of Science (WoS) database using bibliometric analysis and to reveal trends in this area. In line with this goal, a total of 1,615 studies published between 1980 and 2022 were identified in the Web of Science (WoS) indexes. Descriptive and evaluative bibliometric methods were employed in the analysis of these publications, and the results were visualized using VOSviewer software. According to the research findings, studies on school dropout in open and distance education intensified in 2019. The analysis revealed that E.T. Pascarella is the most cited author, and F.D. Pereira has the highest co-authorship network. An examination based on institutions showed that the Universitat Autònoma de Barcelona has the highest number of publications, while the University of Michigan ranks first in terms of citation numbers. Furthermore, in terms of publication productivity and citation numbers, the United States is ranked first, followed by Spain. Looking at the keywords used in articles on the topic, early studies emphasized concepts such as "distance education", "student retention", "attrition", "student success", "social integration", "academic integration", "survival analysis", "education policy", "graduation", and "financial aids". In subsequent years, these were replaced by terms such as "learning analytics", "educational data mining", "systematic review", "engagement", "drop prediction", "decision tree", "student performance", "gamification", "massive open online course-MOOC", and "artificial intelligence". In this context, discussions were conducted within the framework of the literature, and various recommendations were provided based on the obtained findings. [ABSTRACT FROM AUTHOR]

Integrating deep learning methods for multi-element regression analysis poses a challenge in constructing safety evaluations for building construction. To address this challenge, this paper evaluates the integration of construction safety by quantitatively analyzing practitioners' information and on-site construction conditions. The analytic hierarchy process (AHP) method quantifies construction safety capabilities, considering four key aspects: operators' primary conditions, organizational personnel's working conditions, on-site management conditions, and analysis of unsafe behaviors. A comprehensive set of 19 secondary causal factors is constructed. Furthermore, a hybrid model based on bidirectional recurrent neural network (BiRNN) and bidirectional long short-term memory (BiLSTM) is developed for construction safety evaluation, enhancing the model's generalization ability by introducing the Dropout mechanism. Experimental results demonstrate that the fusion of BiRNN and BiLSTM methods outperforms traditional methods in construction safety evaluation, yielding mean squared error (MSE) and root mean squared error (RMSE) values of 0.48 and 0.69 and mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 0.54 and 3.36%, respectively. The case study affirms that BiRNN-BiLSTM can accurately identify potential safety risks, providing reliable decision support for project management. [ABSTRACT FROM AUTHOR]

Dropout from Higher Education (HE), that is, the number of students that totally leave a given HE institution is concerningly high, especially in times of crisis. Institutions struggle to minimize dropout, but limited data is available likely because gathering data from learners who dropped out is sensitive, likely involving private information. This paper presents a case study research on student dropout from a very large Portuguese engineering faculty. The main objectives of this research include to gain a better understanding about the reasons for dropout, from the former student's point of view, and to build a profile for the dropout-at-risk student. The collected data was retrieved from institutional records and from 134 telephonic interviews with former students. The resulting data is analysed in both quantitative and qualitative ways. Results of all gathered dropout data are clustered into three profiles of students who dropout: those that "pull out", those who were "pushed out" and those who "fall out". Findings include that students do not decide to dropout by a simple single reason but rather a set of reasons. This research article includes 5 concrete improvement suggestions that are likely to reduce dropout. The two main suggestions are to better prepare the transition to HE and to make policies more flexible in times of crisis, example more flexible schedule. [ABSTRACT FROM AUTHOR]

Mine water inflow is a significant safety concern in coal mine operations. Accurately predicting the volume of mine water inflow is vital for ensuring mine safety and environmental protection. This study focused on the Laohutai mining area in Liaoning, China, to reduce the reliance on hydrogeological parameters in the mine water inflow prediction process. An integrated approach combining grid search (GS) with the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) model was proposed, and its results were compared with Visual MODFLOW. The grid search was used to optimize the SARIMA model, modeling the linear component of nine years of water inflow data, with the remaining six months of data used for model validation. Subsequently, the prediction residuals from the SARIMA model were input into the LSTM model to capture the nonlinear features in the data and enhance the generalization capability and stability of the LSTM model by introducing Dropout, EarlyStopping, and the Adam optimizer. This model effectively handles long-term trends and seasonal fluctuations in the data while overcoming limitations in capturing periodicity and trends in complex time series data. The results indicated that the GC-SARIMA-LSTM model performs better than the Visual MODFLOW numerical simulation software in predicting mine water inflow. Therefore, without hydrogeological parameters, the GC-SARIMA-LSTM model can serve as an effective tool for short-term prediction, advancing the application of deep learning in coal mine water inflow forecasting and providing reliable technical support for mine water hazard prevention. [ABSTRACT FROM AUTHOR]

The number of students enrolling in distance learning programmes is rising worldwide, making distance education (DE) a significant part of higher education (HE). Transitioning into a study programme involves numerous challenges, especially for distance learners who face higher dropout rates and compromised academic performance compared to traditional on-campus students. However, when students master these challenges, study success becomes more likely. Nevertheless, knowledge about transitioning into DE remains limited. This scoping review aims to compile existing knowledge and enhance understanding of the critical initial phase of DE by answering the research question: "What is known about the transition into DE in HE?". Following the methodological steps outlined in the PRISMA-ScR checklist, we identified 60 sources from five databases, meeting inclusion criteria through a multi-stage screening process. These articles were analysed using qualitative content analysis. We developed a category system with 12 main categories: 1. Process of transition into DE; 2. Reasons for choosing DE; 3. Characteristics of distance learners; 4. Academic success and failure; 5. General assessment of DE; 6. Differences between face-to-face and DE; 7. Advantages of DE; 8. Challenges of DE; 9. Critical life events; 10. Coping strategies; 11. Add-on initiatives; and 12. Recommendations for DE. The results underline the complexity of the transition into DE, which has unique patterns for each student. The article concludes with practical implications and recommendations for supporting the transition into DE. [ABSTRACT FROM AUTHOR]

Although the effects of electromagnetic ion cyclotron (EMIC) waves on the dynamics of the Earth's outer radiation belt have been a topic of intense research for more than 20 years, their influence on rapid dropouts of electron flux has not yet been fully assessed. Here, we make use of contemporaneous measurements on the same L $L$‐shell of trapped electron fluxes at 20,000 km altitude by Global Positioning System (GPS) spacecraft and of trapped and precipitating electron fluxes at 450 km altitude by Electron Losses and Fields Investigation (ELFIN) CubeSats in 2020–2022, to investigate the impact of EMIC wave‐driven electron precipitation on the dynamics of the outer radiation belt below the last closed drift shell of trapped electrons. During six of the seven selected events, the strong 1–2 MeV electron precipitation measured at ELFIN, likely driven by EMIC waves, occurs within 1–2 hr from a dropout of relativistic electron flux at GPS spacecraft. Using quasi‐linear diffusion theory, EMIC wave‐driven pitch angle diffusion rates are inferred from ELFIN measurements, allowing us to quantitatively estimate the corresponding flux drop based on typical spatial and temporal extents of EMIC waves. We find that EMIC wave‐driven electron precipitation alone can account for the observed dropout magnitude at 1.5–3 MeV during all events and that, when dropouts extend down to 0.5 MeV, a fraction of electron loss may sometimes be due to EMIC waves. This suggests that EMIC wave‐driven electron precipitation could modulate dropout magnitude above 1 MeV in the heart of the outer radiation belt. Key Points: Electron precipitation likely driven by electromagnetic ion cyclotron (EMIC) waves is observed within 1–2 hr of electron flux dropoutsPitch angle diffusion rates inferred from low altitude measurements allow estimating the contribution of EMIC wave‐driven losses to dropoutsEMIC wave‐driven electron precipitation could significantly modulate electron flux dropouts above 1.5 MeV and sometimes down to 0.5 MeV [ABSTRACT FROM AUTHOR]

Dropout is a global crisis and an affliction in the United States. This study analyzes graduation rates based on prior academic achievement, attendance, and behavior at an urban American high school in Virginia over 4 years to identify who is (not) graduating and why. Using a correlational, nonexperimental design, four cohorts of graduates were compared. As found, Latinx male English language learners were overrepresented and had dropped out following exclusionary discipline or failure on standardized tests. Examining predictive indicators can clarify why some students do not complete on time or quit. Implications for practice, policy, research, and preparation are considered. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing technologies have been pivotal in advancing the development of algorithms for clustering heterogeneous cell populations. Existing methods for utilizing scRNA-seq data to identify cell types tend to neglect the beneficial impact of dropout events and perform clustering focusing solely on quantitative perspective. Here, we introduce a novel method named scQA, notable for its ability to concurrently identify cell types and cell type-specific key genes from both qualitative and quantitative perspectives. In contrast to other methods, scQA not only identifies cell types but also extracts key genes associated with these cell types, enabling bidirectional clustering for scRNA-seq data. Through an iterative process, our approach aims to minimize the number of landmarks to approximately a dozen while maximizing the inclusion of quasi-trend-preserved genes with dropouts both qualitatively and quantitatively. It then clusters cells by employing an ingenious label propagation strategy, obviating the requirement for a predetermined number of cell types. Validated on 20 publicly available scRNA-seq datasets, scQA consistently outperforms other salient tools. Furthermore, we confirm the effectiveness and potential biological significance of the identified key genes through both external and internal validation. In conclusion, scQA emerges as a valuable tool for investigating cell heterogeneity due to its distinctive fusion of qualitative and quantitative facets, along with bidirectional clustering capabilities. Furthermore, it can be seamlessly integrated into border scRNA-seq analyses. The source codes are publicly available at https://github.com/LD-Lyndee/scQA.

Abstract Background The abandonment of psychotherapeutic treatments is influenced by various factors, including patient characteristics, therapist traits, and the therapeutic relationship. Despite the well-documented importance of these factors, limited empirical research has focused on the role of the therapeutic relationship and the characteristics of therapist-patient dyads in predicting treatment dropout. This study protocol outlines a longitudinal research project aimed at predicting dropout and non-response in psychotherapy for individuals with personality disorders. The research seeks to identify predictive factors related to psychotherapy outcomes, focusing on patient, therapist, and dyadic elements. Specifically, the study will examine the influence of therapist characteristics (e.g., personality traits, countertransference, responsiveness) on treatment outcomes, explore the impact of relational factors (e.g., treatment expectations, epistemic trust, therapeutic alliance) on therapy effectiveness, and assess how the therapeutic alliance within therapist-patient dyads affects the likelihood of dropout and non-response. Methods The longitudinal study will include 100 therapist-patient dyads (200 participants) recruited from various Mental Health Services in Milan, Italy. Validated instruments will be administered to both patients and therapists at four-time points: T0 (baseline), T1 (3 months), T2 (6 months), and T3 (1 year). Data will be collected at baseline and at the one-year mark to evaluate the relationships between therapist, patient, and dyadic factors and treatment outcomes. Discussion Identifying predictive variables associated with high dropout rates can help preempt treatment discontinuation, reducing the financial and operational burdens on mental health services. Understanding these factors will enable the development of targeted interventions to improve treatment engagement and reduce attrition. This approach could enhance outcomes for individuals with personality disorders and lead to more efficient resource allocation and sustainable delivery of mental health care.

Structural racism and the ongoing modern-colonizing project in Latin America would not be the same without the contributions of Modern Western Science. Contradictorily, it is also through the sciences — especially social sciences — that colonialities have been challenged. Inspired by decolonial criticism, we tell the life story of Waxihô Karajá, an indigenous Karajá Xambioá man who entered and abandoned a Biology course. The results illustrate how Iny indigenous education contributed to the development of Waxihô Karajá's interest in Biology. Our analysis also reveals that the low quality of basic education offered in indigenous communities; the unpreparedness of higher education institutions to receive diversity; the difficulties experienced by young people with low-education backgrounds and the energy expended by cultural minorities to combat a myriad of colonial violences through student activism can compromise academic success. Results indicate that the approach and the departure from natural science courses by young indigenous people seem to have reasons that are sometimes similar and sometimes different from those listed in the research with young people from the national society. There is an urgent need to reorient university retention policies to promote the academic success of indigenous students in natural sciences courses.

Abstract In 2022, one in four vocational education and training (VET) contracts resulted in premature termination—a potential sign of inefficiency in the training of urgently needed skilled professionals. This study focuses on the level of perceived informedness of trainees and its potential influence on dropout. This includes trainees’ perceived level of information about the characteristics and requirements of an occupation, which may result from various sources of information in the form of social contacts. We draw on theories from the field of career choice and job search to argue that a better level of informedness can reduce uncertainty and enable individuals to make better-informed decisions about their careers. With better informedness, there is a higher probability of finding a suitable match between a trainee and their chosen occupation, ultimately increasing their likelihood of job persistence. This should also lead to a lower probability of dropout among VETs. Using data from the National Educational Panel Study, starting cohort 4, we employed event history analysis. We show that good perceived informedness on the requirements of one’s vocational training and the perception of good school-based vocational preparation reduce the probability of dropping out. In contrast, good informedness about alternative training programs increases dropout risk. We also see that the distinction between closer or more distant groups of people from the social network (strong or weak ties) who provide information about training is not decisive.

Abstract Taking the perspective of career choice as a lifelong, iterative, constructive, and agentic process, the present study focuses on the development of vocational aspirations of nursing trainees; that is, thoughts about a long-term perspective in nursing (i.e., persistence), ideas of finishing the training but changing into another profession after some time (i.e., withdrawal), and decisions to terminate the training before completing the programme through a final examination (i.e., dropout). In order to generate detailed insights about the dynamics behind the development of such aspirations during the initial training phase, a qualitative, longitudinal, within-subject study design based on grounded theory was employed. The results mainly show that social interactions with more experienced nurses, practical work experiences, encounters with environments that are either conducive to learning or not, the satisfaction of different needs (e.g., autonomy, competence, belonging, sense of meaningfulness), as well as the associated feelings of well-being affect how vocational aspirations develop over the first year of training. In addition, the study identifies four different patterns of how trainees typically oscillate between thoughts of staying in nursing and leaving the profession in the short or long run: (a) arriving and wanting to stay, (b) staying as a transitional passage, (c) seeking to stay, and (d) exiting as a knee-jerk reaction. The patterns present evidence of a variety of approaches regarding how trainees deal with certain experiences during their training and how the combination of experiences might affect young professionals’ subsequent career choices.

Abstract 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

This study investigates the role of social capital within the university context in retaining working students. It specifically examines the effects of university social capital factors—such as teacher–student relationships, peer networks, and support services—on the dropout intentions of working students, emphasizing the mediating role of employability trust. Using a sample of 1902 working students from the Eurostudent VII survey, this study employed factor analysis techniques and structural equation modeling to derive its findings. The results indicated that university social capital significantly reduces dropout intentions among working students. Strong teacher–student relationships, satisfaction with support services, robust peer networks, and high employability trust positively influence this social capital. There is a statistically significant negative association between teacher–student relationships, peer networks, employability trust, and dropout intentions. Furthermore, the findings reveal that without enhancing students’ employability trust, the effectiveness of support services might be limited. These findings not only contribute to the discourse on student retention and the development of university social capital but also provide practical insights for higher education strategies aimed at supporting working students.

Abstract 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.

This systematic review investigates the effectiveness of motivational strategies on learner engagement and retention rates in Massive Open Online Courses (MOOCs). Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 140 studies published between 2014 and 2023 from key academic databases. The objective was to identify and evaluate motivational strategies that significantly reduce MOOC dropout rates. Our findings reveal that personalized learning, interactive content, and peer collaboration are strongly correlated with increased learner engagement and persistence. These strategies align well with learners' intrinsic goals, enhancing their educational experience and adherence to courses. The review also identifies gaps, such as the need for longitudinal studies and culturally tailored motivational strategies, offering a refined agenda for future research in MOOC education. This study contributes to the field by systematically synthesizing existing research, providing new insights into effective educational strategies, and highlighting areas for improvement in MOOC design and implementation. Doi: 10.28991/ESJ-2024-SIED1-08 Full Text: PDF

Learner dropout remains a challenge in Eswatini despite the introduction of free primary education by the government. The study established the factors that contribute to learner dropout at the grade six level in the Manzini region. An interpretivist research philosophy and a qualitative research approach were adopted to collect the study’s data. A case study design made it possible to collect detailed data from purposively selected participants in three public primary schools. Interviews were held with two head teachers while nine grade six teachers took part in focus group discussions. Findings revealed that school-related factors like overcrowded classrooms, poor performance and non-availability of practical subjects that aligned with learners’ interests and abilities contributed to learner dropout. Findings further revealed that poverty, child labour, peer pressure, teenage pregnancy, lack of parental involvement, and initiation into cultural practices and activities contributed to learner dropout. The study recommended that the government should build more schools and classrooms and hire more qualified teachers to reduce overcrowding in classrooms and the distance walked by learners to school. The government should also support needy learners from disadvantaged backgrounds with basic necessities such as sanitary pads for girls. The Ministry of Education should introduce practical subjects like Carpentry, Art, Music and Dance to cater for the interests and abilities of different learners. Schools should promote parental involvement and core curricula activities to engage, motivate and retain learners. The study contributes to the body of knowledge by suggesting strategies that can mitigate learner dropout which is a widespread challenge in the SADC region and other African countries.

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. [ABSTRACT FROM AUTHOR]

Introduction: Previous research has uncovered various inequalities for immigrant students in education systems worldwide. Yet, the degree to which diverse background factors, such as socio-economic status and prior educational trajectory, contribute to these educational disadvantages remains uncertain. Methods: Using data from the Flemish administrative database (2009-2019) on 48,340 mainstream students and 1,022 Newly Arrived Migrant Students (NAMS) across 685 schools, this study examines NAMS' passing and dropout rates in Flemish secondary education in comparison to the outcomes of their peers without a recentmigration background. Data included demographic background variables, school variables, and indicators of educational outcomes. Results: A cross-classified multilevel regression analysis showed that, after controlling for background variables, NAMS still show higher probabilities of failing a year and of dropping out. Moreover, the impact of SES is stronger for native students than for NAMS. Discussion: These results suggest the existence of a migrant background effect disadvantaging NAMS in Flemish secondary education. [ABSTRACT FROM AUTHOR]

AbstractCAPTCHAs are commonly used as gatekeepers to protect online services from automated bots. Previous CAPTCHA evaluations have solely focussed on whether CAPTCHA challenges are unbreakable by bots but solvable by humans and have ignored a fundamental question: what effect do these gatekeepers have on their human users? Our study proposes a more realistic approach that complements existing CAPTCHA evaluations by measuring CAPTCHA’s effect on the main task that the user wants to complete. Through our experimental setup, we show that failing to correctly answer CAPTCHAs can turn away a significant percentage of legitimate, human users. We also show that these failures have temporary knock-on effects on the quality of tasks that users later perform. Methodologically, our study also reveals limitations of current approaches to batch evaluations of CAPTCHAs that do not accurately capture the effects that ordering and variance in difficulty have on users’ CAPTCHA-solving performance. [ABSTRACT FROM AUTHOR]

We introduce an online, model‐free algorithm to address the ℒ2$$ {\mathcal{L}}_2 $$ gain optimal tracking problems in discrete linear networked control systems. The algorithm is specifically proposed to handle stochastic information dropout in the feedback loop. Our goal is to design a control law that achieves the system output reference tracking while attenuating the effect of the disturbance input. We first construct an augmented system consisting of the original system and the command generator system. Then, the performance index is expressed in a quadratic form taking into account packet loss. Next, we obtain the optimal solution by solving the dropout generalized algebraic riccati equation (GARE). Finally, a Q$$ Q $$‐learning algorithm is utilized to estimate the control and disturbance feedback gains of the system, using only measurement data with unknown system dynamics in the presence of dropout. Two algorithms are tested on a numerical example to demonstrate the validity and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This study aimed to provide the first comprehensive evidence on the prevalence and predictors of dropout in psychological interventions for pathological health anxiety. A database search in Web of Science, EMBASE, PubMed, Scopus, PsycINFO and the Cochrane Central Register of Controlled Trials identified 28 eligible randomized controlled trials (40 intervention conditions; 1783 participants in the intervention condition), published up to 18 June 2024. Three‐level meta‐analytic results showed a weighted average dropout rate of 9.67% (95% confidence interval [CI] [6.49%, 14.17%]), with dropout equally likely from treatment and control conditions (odds ratio = 1.07, 95% CI [0.80, 1.44]). Moderator analyses indicated no statistically significant effects of study, participant, treatment or therapist characteristics, except for the country of study. These findings suggest that the average dropout rate is relatively low compared with those reported for other mental health conditions and highlight the importance of considering cultural and societal factors when evaluating treatment adherence. Future research should continue to explore the complex and multifaceted factors influencing dropout to improve the design and implementation of psychological interventions for pathological health anxiety. [ABSTRACT FROM AUTHOR]

Este artículo aborda el conjunto de desafíos teóricos y metodológicos que aún no han sido superados por el campo científico que estudia la deserción en la educación superior, aquí tratada metafóricamente como una maldición. El objetivo fue sistematizar el aporte de la bibliografía que señala las razones por las cuales la larga trayectoria teórica y aplicada no ha logrado el resultado esperado, es decir, la reducción de índices. Para ello, se utilizó una revisión narrativa de la bibliografía, buscando recoger aportes de varias obras de referencia en casi un siglo de historia del campo. Los resultados apuntaron a la presencia de desafíos en la definición del objeto (falta de consenso e inespecificidad). Pero también señalaron desafíos metodológicos como la falta de un enfoque longitudinal, sistémico y combinado para la investigación cualitativa y cuantitativa. [ABSTRACT FROM AUTHOR]

BACKGROUND: Polycystic Ovary Syndrome (PCOS) is a medical condition that causes hormonal disorders in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS mainly suffer from extreme weight gain, facial hair growth, acne, hair loss, skin darkening, and irregular periods, leading to infertility in rare cases. Doctors usually examine ultrasound images and conclude the affected ovary but are incapable of deciding whether it is a normal cyst, PCOS, or cancer cyst manually. OBJECTIVE: To have access to the high-risk crucial PCOS and to detect the condition and the treatment aimed at mitigating health hazards such as endometrial hyperplasia/cancer, infertility, pregnancy complications, and the long-term burden of chronic diseases such as cardiometabolic disorders linked with PCOS. METHODS: The proposed Self-Defined Convolution Neural Network method (SD_CNN) is used to extract the features and machine learning models such as SVM, Random Forest, and Logistic Regression are used to classify PCOS images. The parameter tuning is done with lesser parameters in order to overcome over-fitting issues. The self-defined model predicts the occurrence of the cyst based on the analyzed features and classifies the class labels effectively. RESULTS: The Random Forest Classifier was found to be the most reliable and accurate among Support Vector Machine (SVM) and Logistic Regression (LR), with accuracy being 96.43%. CONCLUSION: The proposed model establishes better trade-off compared to various other approaches and works effectually for PCOS prediction. [ABSTRACT FROM AUTHOR]

Objective: While biologics have been used for the patients with psoriatic arthritis, there remains to be unknown concerning long-term retention rates. This study aims to present real-world data about long-term retention rates of biologics for the patients with psoriatic arthritis, and to undertake an analysis of the contributing factors. Methods: We examined retention rates and the reasons for discontinuation for biologics (adalimumab, certolizumab pegol, secukinumab, and ixekizumab) in 146 prescriptions (of which, 109 prescriptions were as naive) at our hospital since March 2010. Results: Throughout the entire course of the study, the 10-year retention rates were approximately 70% for adalimumab, 50% for ixekizumab, and 40% for secukinumab. When evaluating retention rates in the biologic-naïve subgroups, the 10-year retention rates were all approximately 70%. Regarding certolizumab pegol, the 3-year retention rate was approximately 75%. For adalimumab, a higher degree of arthritis at the initiation of treatment was found to correlate with an increased likelihood of secondary inefficacy. The main reason for discontinuation was secondary inefficacy, except for ixekizumab. Conclusions: Each biologic exhibited a favourable long-term retention rate. The main reason for discontinuation was secondary inefficacy. Regarding adalimumab, secondary inefficacy was linked to the extent of arthritis upon treatment initiation. [ABSTRACT FROM AUTHOR]