Early Dropout Research Articles

Risk and protective factors for early dropout from telephone monitoring for individuals with drug convictions in community mental health centers in Japan

IntroductionIndividuals involved with the criminal justice system face challenges in receiving and maintaining substance use disorder (SUD) treatment and support. Although telephone monitoring (TM) could reduce these barriers, data on TM for community-dwelling individuals involved with the criminal justice system and research on individuals who drop out of TM are scarce. We examined the factors associated with dropping out early from the Voice Bridges Project, which provides TM for individuals on probation for drug-related convictions through community mental health centers in Japan. MethodsParticipants (n = 546) were individuals aged ≥20 years with methamphetamine-related convictions who were on probation. Univariate analyses examine the associations between one-year follow-up status and baseline variables, and multivariate Cox proportional hazards regression analyses identify the risk and protective factors associated with dropping out. Stratified analyses report results based on sex and halfway-house residency. ResultsThe one-year dropout rate was 43.6 % (n = 238). Multivariate analysis identified two risk factors for dropping out—halfway-house residency and suicide attempts in the past year, and two protective factors—higher education and the current use of SUD services. Sex-stratified analyses showed that halfway-house residency was a risk factor for both men and women. Attempted suicide was a risk factor for women. Conversely, higher education and current use of SUD services were protective factors for men. ConclusionsOur results identify unique risk factors for women, such as a recent history of suicide attempts, and distinctive protective factors for men, including higher education and current use of SUD services, emphasizing the importance of sex-specific approaches. Furthermore, the study reveals that irrespective of sex, vulnerable individuals, such as halfway-house residents, are at a higher risk of dropping out from TM.

Personalising circadian hygiene educational initiatives aimed at university students-"He who has ears to hear, let him hear".

The aim of the present study was to characterise "early drop-outs" (n = 3185) out of a group of university students (n = 7766) engaged in an ongoing circadian education initiative, to evaluate its efficacy and direct its developments. The initiative is aimed at improving sleep timing/quality through one of two sets of circadian hygiene advice covering the timing of sleep, meals, exercise and light exposure, and it has already been shown to have a positive effect on sleep timing. This second, interim analysis confirmed the high prevalence of disturbed night sleep and social jetlag amongst students at Padova University. Three-thousand, one-hundred and eighty-five (41.0%) students were early drop-outs. These were more commonly males (46.4 versus 37.6%; χ2 = 58, p < 0.0001), had later sleep-wake habits, more daytime sleepiness and worse night sleep quality. Chronotype distribution was also different, with a slight but significantly higher proportion of extremely evening/evening types amongst early drop-outs (χ2 = 10, p < 0.05). These results suggest that the more evening the student, the lower their likelihood of choosing/being able to follow circadian advice.

Effect of the Social Safety Net on Child Labor and School Retention in Bangladesh: Empirical Evidence from Hies 2016

ABSTRACT: In Bangladesh Social Safety Net (SSN) interventions are intended to achieve specific goals, e.g., reduction in poverty, decrease in early school dropout, and nutrition supplementation. This study investigates the effectiveness of SSN participation on less explored goals of child labor reduction and school retention in Bangladesh. Empirical evidence is provided to evaluate the success of existing programs to combat child labor and early school dropout. This study utilizes the Household Income and Expenditure Survey (HIES), 2016 of Bangladesh. We employ the Propensity Score Matching (PSM) framework to show the causal relationship between SSN participation and its impact on the use of child labor and school retention. Given the nature and availability of data, the matching method provides us an estimate of the impact of SSN based on a quasi-experimental setting. We estimate counterfactuals based on observed characteristics of the household that affect both SSN participation and our outcome variables. Our results suggest that households that receive SSN tend to employ less child labor domestic or paid compared to their counterparts. Our Propensity Score Matching (PSM) results show that SSN participation reduces child labor prevalence in Bangladesh by 2.4% to 2.9%. This is an indication that families essentially substitute SSN income with child labor earnings. Thus, SSN programs in Bangladesh are effective in reducing child labor though the magnitude is not overwhelming. On the contrary, our results show that children from SSN-participating families have a higher incidence of early school dropout. This may be due to inefficiency or poorly designed programs that effectively keep the children in school. We estimate early school dropout is higher by 3.6% to 6.6% for households that receive SSN compared to the control group. Our findings are important for policymakers to redesign the programs that retain children in school. We suggest targeted interventions that will help to keep these children in school, which is conducive to human capital accrual and increasing lifetime earnings. Additionally, our insights will assist policymakers in rethinking SSN coverage, efficient selection of beneficiaries, and initiation of the correct intervention.

Treatment Non-adherence Patterns Among Patients With Mental Illness: A Study From the District Mental Health Care Center in India.

The success of any medical intervention, including mental health treatment, depends largely on patient adherence to the prescribed regimen. In psychiatric illnesses, one of the biggest problems is getting people to adhere to their treatment schedule, representing a treatment gap that increases the burdens of patients, families, communities, and countries. Globally, it has become necessary for community health organizations to actively work towards reducing this gap and treatment non-adherence. Therefore, in this study, we aimed to examine treatment non-adherence patterns among patients with mental illness. This work used a retrospective study design and consecutive sampling. The data source was secondary data obtained from the healthcare records of patients registered in the outpatient department of the District Mental Health Care Center, India, from January 2022 to December 2022. Out of a total of 883 patients recruited for the study, 35.7% (n=315) were on regular follow-up over a duration of more than one year. Among patients with severe mental illness, 46% (n=46) had regular follow-ups and were compliant with therapy. About 49% of patients (n=433) discontinued their treatment after the initial contact with the therapist, with the highest rate among those with substance use disorders (77.0%; n=57). The remaining 15.3% (n=135) of recruited patients discontinued their follow-up appointments over a duration of 1 week to 12 months. Overall, 64.3% (n=568) of the recruited patients discontinued their treatment within one year. There was considerable early treatment dropout among patients with mental illness. However, this treatment discontinuation can be avoided because the individual identities of these patients are well-known to the therapist or facility, as they have had at least one interaction with the therapist. In order to improve treatment adherence, patients with mental illnesses must receive consistent support through community outreach programs, home visits, and new strategies to promote treatment compliance.

Treating Children and Adolescents with Obesity: Predictors of Early Dropout in Pediatric Weight-Management Programs.

Possible therapeutic failure of pediatric obesity is influenced by the high dropout rate. The aim of this study was to evaluate the rate of dropout and the rate of weight loss over the 24 months of follow-up. The retrospective, single-center study, involved 489 patients followed for obesity in the period 2016-2020. Patients' auxological data and blood samples were collected during the first (V1) and last visit (V2). Dropout was defined as a follow-up of less than 12 months and/or including less than one visit every 6 months. Patients were divided into two groups and compared: Group A of dropout (297 patients) and Group B of non-dropout (192 patients). In the follow-up period, which had a mean duration of 24 months, the dropout rate was 60.7%. In Group A, the percentage of patients with BMI ≥ 3 SD at V2 was significantly higher than that in Group B. In Group B, the percentage of patients with pathological HOMA-IR and with fasting glucose >100 mg/dL was higher than group A. The probability of dropout was positively associated with pubertal stage and negatively with impaired fasting glycemia and pathological insulinemia at V1. The study demonstrated a high dropout rate during follow-up, mainly among adolescents and patients with no glucometabolic alterations.

Genetic and shared environmental factors explain the association between adolescent polysubstance use and high school noncompletion.

Examine the nature of the relationship between adolescent polysubstance use and high school noncompletion. Among a sample of 9,579 adult Australian twins (58.63% female, Mage = 30.59), we examined the association between the number of substances used in adolescence and high school noncompletion within a discordant twin design and bivariate twin analysis. In individual-level models controlling for parental education, conduct disorder symptoms, childhood major depression, sex, zygosity, and cohort, each additional substance used in adolescence was associated with a 30% increase in the odds of high school noncompletion (OR = 1.30 [1.18, 1.42]). Discordant twin models found that the potentially causal effect of adolescent use on high school noncompletion was nonsignificant (OR = 1.19 [0.96, 1.47]). Follow-up bivariate twin models suggested genetic (35.4%, 95% CI [24.5%, 48.7%]) and shared environmental influences (27.8%, 95% CI [12.7%, 35.1%]) each contributed to the covariation in adolescent polysubstance use and early school dropout. The association between polysubstance use and early school dropout was largely accounted for by genetic and shared environmental factors, with nonsignificant evidence for a potentially causal association. Future research should examine whether underlying shared risk factors reflect a general propensity for addiction, a broader externalizing liability, or a combination of the two. More evidence using finer measurement of substance use is needed to rule out a causal association between adolescent polysubstance use and high school noncompletion. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Towards Sustainable Education: A Machine Learning Model for Early Student Dropout Prediction in Higher Education Institutions

Sustaining learners through an education cycle is a challenge for institutions at all levels. For higher education institutions, learners are presumed to be mature enough to complete their study courses. However, the challenge of student dropouts is prevalent. This paper seeks to address the key question of why students continue to drop out of learning institutions despite interventions undertaken by stakeholders. The attrition rates are a major concern that requires immediate attention if sustainable education is to be achieved. Dropping out of school is attributed to both individual factors and external factors. However, both require mitigation to save the future of education. This paper presents an analysis of challenges leading to student dropouts sampled from five institutions within the central region of Uganda (532 respondents). In addition, we leveraged the power of artificial intelligence (AI) to design and present a machine learning model for early student dropout prediction so that early interventions can be undertaken. The study adopted the design science methodology to scientifically support the design and validation of the machine learning student dropout prediction model. The early warning model presents key performance indicators to signal whether a student is predisposed to drop out or on course to completion. This way corrective intervention can be undertaken early enough for likely dropout. The validation experiment was conducted on a sample of 523 from the five institutions predicated a dropout of 10%. This proved the concept and the capacity of the model to predict learner dropout from university.

Educational Marginalization in Rural Pakistan: Sociocultural Economic Determinants for Policy Implications

In the socioeconomic context of the Koh-e-Suleman Tamun Qaisrani region of District Dera Ghazi Khan, the research examined the critical issue of early school departure and its multifaceted implications for the community and broader economic progress. Addressing a core aspect of human capital formation and societal development, the study scrutinized the impact of educational discontinuation on a country's overall advancement, emphasizing the linkage between early school dropout and a low literacy rate with manifold concomitant social challenges. Using a methodological approach encompassing convenient sampling techniques, data was elicited from 250 respondents via questionnaires. The research's analytical focus unearthed a clear correlation between parental income and literacy levels with students' dropout ratios and early school leaving tendencies. Key determinants were identified, including familial socioeconomic background, peer relationships, cultural phenomena such as early marriages, and logistical barriers like school distance. The findings divulged that lower parental income and literacy contribute to higher dropout rates and augmented probabilities of premature educational withdrawal. The study thus expounded a sociological critique of these systemic failures, accentuating the state's onus to ameliorate these issues. Recommendations were proffered, accenting the government's obligation to furnish elementary school amenities and institute financial aid measures for economically disadvantaged students, thereby cultivating an environment conducive to the amelioration of this profound social malaise.

Exploring Teachers’ lived Experiences towards Students’ Engagement in Classroom: A Qualitative Approach

Engagement of students in classroom is of vital importance and has been used to predict student achievement for quite some time. There is growing concern among educators about the impact of disengagement of students which is manifested in early dropouts, regular absence from class and poor behavior, ultimately leading to low levels of academic achievement. The purpose of this study is to explore the phenomenon of students’ engagement as experienced by the teaching faculty in the classrooms of different educational institutions of Uttar Pradesh. Qualitative approach was used to highlight the shared experiences of the participants which led to the emergence of four themes. The results of this study reveals that there is a lack of consensus among respondents on a specific definition with regards to Student Engagement. Furthermore, student interaction is fundamental to Student Engagement. In addition, Student Engagement has a positive impact on Teacher Motivation and last but not the least, Teacher's Self-Efficacy has a positive impact of Student Engagement. This study is an effort to build a shared understanding about the concept of student engagement among the teaching fraternity, department heads, college and university administrators and other stakeholders in the context of educational institutions of Uttar Pradesh and this may help them to prepare a blueprint for tackling the issue student disengagement in classroom.

Численное исследование полидисперсных двухфазных течений в осесимметричном сопле Лаваля с учетом силы Магнуса, действующей на вращающиеся капли

The available papers on two-phase flows in de Laval nozzles are focused on the effect of various initial conditions, models of coagulation, fragmentation, and rotation of droplets on their local and integral characteristics. Some papers note that due to the difference in the velocities of the gas and droplets along the nozzle axis, the Magnus force acts on the rotating droplets perpendicular to the specified difference and deflects the trajectories of the droplets toward the nozzle wall. In an earlier paper, the authors proposed a mathematical model of a two-phase flow in an axisymmetric de Laval nozzle that accounts for the Magnus force on the basis of the kinetic equation. The present article is devoted to a numerical study of a polydisperse two-phase flow in a de Laval nozzle with account for the coagulation, fragmentation, and rotation of droplets and the Magnus force. This study is based on the “monodisperse” model of fragments developed by I.M. Vasenin and A.A. Shreiber, the method of labeled drops (the Lagrange method), and the finite-difference schemes of second order accuracy. The calculations are carried out for a test nozzle configuration with condensate formation on the nozzle wall both with and without the Magnus force accounted. The calculated results show that the Magnus force has different impacts on the trajectories of droplets of various sizes. It should be noted that the limiting trajectories of the droplets approach the nozzle wall due to the Magnus force, resulting in the earlier dropout on the wall. Therefore, when designing the divergent section of the nozzle, it is necessary to consider the revealed approach of the location of the dropout, which is associated with the Magnus force, to the nozzle throat.

Enhancing the Early Student Dropout Prediction Model Through Clustering Analysis of Students’ Digital Traces

Enhancing the Early Student Dropout Prediction Model Through Clustering Analysis of Students’ Digital Traces

Students' perspectives on their early dropout of medical school.

Enrolling in medical school launches a more demanding and stressful way of life for newly admitted students. Some students will struggle academically and will ultimately drop out from medical school. The study aims to understand the perspectives that dropped-out students have and their opinion regarding possible preventative solutions. This study uses a mixed-method approach. The results of students' examinations in the first 2 years of admission (within four academic years from 2016 until 2020) were collected from the medical school records. A questionnaire was developed to investigate the causes of early attrition during the study period. All dropped-out students who were admitted were included in this study. The study included 479 admitted students, 349 (72.9%) were females; 152 (31.7%) students dropped out in the premedical program (first two years); 104 (68.4%) of the dropped-out students were females. Thirty-five (23%) students responded to the questionnaire. Twenty-eight (80%) respondents believed that their low academic performance and leaving medical school was primarily related to psychological problems, 19 (54%) of respondents reported leaving for social reasons, and lack of self-organization in 13 (37.1%) respondents. Human anatomy was recorded as the most difficult course in the first year by 21 (63.6%) students. The cause of attrition is often multifactorial and complex. Psychological problems are one of the important factors in students with low academic performance and dropping out of the students. Some difficult courses in the curriculum may affect the students' academic performance leading to their attrition.

Be SMART: Promoting goal setting with students at-risk of early school leaving through a mentoring program

Mentoring programs are a very popular tool in educational contexts. Over the years, many schools have been using mentoring programs to improve academic adjustment and counter the disengagement of students at risk of early school leaving. Since 2016, the Portuguese Ministry of Education has implemented a universal educational policy focused on the implementation of mentoring sessions in public schools. This policy targets students older than 12 years old with multiple school retentions and diverse characteristics likely to place them at risk of early dropout. In the context of this policy, a school-based mentoring program to promote SMART goal-setting skills through the training of self-regulation was developed to be implemented with elementary and middle school students at-risk of early dropout. The 20-session program was implemented over two school terms. Throughout the program, we conducted a multiple case study to examine how students’ goals and SMART goal-setting processes may change after their enrollment in the mentoring program. Students’ goals were collected pre- and post-program through the fulfillment of life map diagrams. Post-program overall data indicated that most students set more academic and fewer non-academic goals. However, a granular analysis showed that a group of older students (17 years old) did not set any academic goal in both pre- or post-program moments; furthermore, only younger students (aged between 12 and 16 years old) integrated SMART characteristics into their academic goals. Limitations and future research directions, as well as implications for practice, are provided.

THE PREDICTIVE ROLE OF ACADEMIC IDENTIFICATION AND TRAIT ANXIETY FOR EARLY ACADEMIC DROPOUT INTENTIONS

In this study, we test the association of academic identification (AI) and trait anxiety (TA) with early academic dropout intentions (EADI). Additionally, we test the impact of the interaction between AI and TA, while controlling for socio-demographic covariates specific to the participants, over EADI (i.e., gender, parents` education background, and educational expectancies). Our sample consisted of 439 first-year university students. We used the social identification theoretical background and results show that AI is associated with low scores of EADI. Still, the high trait anxiety context in which students find themselves in their first year is a risk factor, when AI is low or average. Results also show gender differences (i.e., men have a higher risk of EADI, compared to women). Results highlight the need to invest more in the development of AI to counter and thwart the risk of emphasizing AT for young people.

Most and Least Learned Competencies of Kindergarten Pupils in Sreya: Bases for Resource Materials Enhancement

The main focus of the study is 1) To know the profile of the kindergarten teachers 2)To know the percentage of kindergarteners who acquired school readiness 3)To know the least and most learned competencies of kindergartners 4)To determine the differences of students school readiness between teacher’s employment status, teacher’s educational background, and type of school. The country’s K-12 curriculum has become one of the most talked about issues not only within the Department of Education but across the nation, so to speak. Different programs, resource materials, teaching devices, activities, and training for teachers have been devised and are still being conducted in preparation for the full implementation of the said new curriculum. Under the K-12 curriculum, the Kindergarten program was added to the existing basic education program that the Deped utilizes. The main concern of the kindergarten program is the school readiness of the young learners. Learners must be physically, intellectually, and emotionally prepared before they enroll to formal education, since one the most common reasons for the early drop-outs of school-age children is lack of interest in learning. Cognizant of the fact that the main goal of the kindergarten program is the smooth transition from a play-based approach into formal education. The kindergarten curriculum was designed to cope with the needs of the young learners. Likewise aimed at ensuring the development of young children and that they enjoy their experiences in the kindergarten program, the thematic and play-based approaches to learning are utilized. Initially, the program was met with cynicism specifically from the community and some parents. They were hesitant to enroll their children in the kindergarten program, for they viewed this as just an additional grade level in the education of their children. The researcher identified the least and most learned competencies in the result of the School Readiness Year End Assessment (SReYA) and proposed a resource materials enhancement to address the least learned competencies of kindergarteners. The most learned competencies are recognition of shapes, problem-solving and counting numbers from 1-10 while the least learned competencies are listening comprehension, Identifying upper- and lower-case letters, letter-sound corresponded, and recognition of numbers. The researcher identified the differences of student’s school readiness in the result of the study. There is a significant difference to the school readiness of kindergarteners when grouped according to the employment status of teachers, length of service, educational background, and type of school. The researcher suggested additional activities to reinforce the development of the least learned competencies of kindergarteners Keywords: Assessment, Learning Competencies, Kindergarten, School Readiness

Predicting Dropout in Programming MOOCs through Demographic Insights

Massive Open Online Courses (MOOCs) have gained widespread popularity for their potential to offer education to an unlimited global audience. However, they also face a critical challenge in the form of high dropout rates. This paper addresses the need to identify students at risk of dropping out early in MOOCs, enabling course organizers to provide targeted support or adapt the course content to meet students’ expectations. In this context, zero-time dropout predictors, which utilize demographic data before the course commences, hold significant potential. Despite a lack of consensus in the existing literature regarding the efficacy of demographic data in dropout prediction, this study delves into this issue to contribute new insights to the ongoing discourse. Through an extensive review of prior research and a detailed analysis of data acquired from two programming MOOCs, we aim to shed light on the relationship between students’ demographic characteristics and their likelihood of early dropout from MOOCs, using logistic regression. This research extends the current understanding of the impact of demographic features on student retention. The results indicate that age, education level, student status, nationality, and disability can be used as predictors of dropout rate, though not in every course. The findings presented here are expected to affect the development of more effective strategies for reducing MOOC dropout rates, ultimately enhancing the educational experience for online learners.

Improving deep learning performance by augmenting training data

Satellite image recognition is a crucial application of computer vision that has the po-tential to be applied in various fields such as disaster management, agriculture, and urban planning. The objective of this study is to determine the optimal amount of input data required and select the most effective methods of augmentation necessary for training a convolutional neural network (CNN) for satellite image recognition. To achieve this, we perform a series of experiments to investigate the effect of input data quantity on several performance metrics, including model accuracy, convergence, and generalization. Additionally, we explore the impact of various data augmentation techniques, such as rotation, scaling, and flipping, on model performance. The study suggests several strategies for identifying the saturation point and mitigating the effects of overtraining, in-cluding early stopping and dropout regularization. The findings from this study can significantly contribute to the development of more ef-ficient satellite recognition models. Furthermore, they can help improve the performance of existing models, in addition to providing guidance for future research. The study emphasizes the importance of carefully selecting input data and augmentation methods to achieve optimal performance in CNNs, which is fundamental in advancing the field of computer vision. In addition to the above, the study investigates the potential of transfer learning by pre-training the model on a related dataset and fine-tuning it on the satellite imagery dataset. This approach can reduce the amount of required data and training time and increase model performance. Overall, this study provides valuable insights into the optimal amount of input data and augmentation techniques for training CNNs for satellite image recognition, and its findings can guide future research in this area.

Predicting Early Dropout in a Digital Tobacco Cessation Intervention: A Replication and Extension (Preprint)

Predicting Early Dropout in a Digital Tobacco Cessation Intervention: A Replication and Extension (Preprint)

Functional Precision Medicine Vs Genomics Vs Clinical Experience: Feasibility Results from the Multicentric, Prospective, Randomized Controlled Exalt-2 Trial

Background Aggressive hematological malignancies have a dismal prognosis in the relapsed/refractory setting representing an unmet medical need. Despite the advance of numerous novel therapeutics to clinical routine, treatment selection at advanced disease stages typically is challenging because of chemoresistance, cumulative toxicity, patient-specific factors, and a lack of evidence-based protocols in heavily pretreated patients. Precision medicine approaches promise to extend the number of candidate drugs beyond the standard of care, ultimately providing a treatment rationale in situations where evidence-based treatment options are scarce. We have previously shown that single-cell functional precision medicine (scFPM) is clinically feasible and efficacious in patients with aggressive hematological cancers who exceeded all standard therapy lines (Snijder et al. Lancet Haematology 2017, Kornauth et al, Cancer Discovery 2022). Inspired by this proof of concept, we launched a multicentric, prospective, randomized controlled trial to assess if genetic, or functional precision medicine-guided therapy may improve patient outcomes compared to best clinical experience-guided therapy (NCT04470947). Methods EXALT-2 is a multicentric, prospective, randomized controlled trial in five academic hospitals in Austria (Vienna, Graz, Innsbruck, Linz, and Salzburg). The study aims to enroll 150 patients with aggressive hematological malignancies in a relapsed/refractory setting with an overall good performance status (ECOG ≤ 1). After inclusion, a real-time tumor-containing biopsy is obtained to confirm relapse and collect viable tumor samples from each patient. Tumor cells are subjected to single-cell functional precision medicine (scFPM) and comprehensive genomic profiling (CGP) assays. Importantly, scFPM testing comprises flow cytometry- and/or imaging-based high-throughput drug screening. Depending on the randomization arm, therapy is suggested by the multicentric, multidisciplinary EXALT-2 tumor board, which consists of at least 2 hematology specialists, a pathologist, a molecular biologist, and a pharmacist. The EXALT-2 board gives a treatment recommendation either based on i) scFPM or ii) CGP results, or iii) without any precision medicine support (Physicians' Choice, PC). If precision medicine assays fail or do not yield a treatment recommendation, a cross-over between study arms is allowed. An overview of the study design is shown in Figure 1A. Results Feasibility results are available for the first 55 included patients. Of these, 54 patients (98%) underwent real-time biopsy and were randomized with an equal distribution across study arms. 42 patients (76%) received a therapy suggestion from the EXALT-2 tumor board. The majority of these patients suffered from aggressive lymphoma . Finally, 39 patients (71% of the total, 92% of patients with a board recommendation) received treatment. 10 patients were classified as early drop-outs due to death or progression within the first 28 days of board-recommended therapy according to the study protocol. 29 patients (53% of total, 69% of patients with a board recommendation) continued on study and are available for further evaluation. Precision medicine assays were technically feasible in most instances (flow cytometry-based scFPM: 80% of tests; microscopy-based scFPM: 52% of tests; CGP: 86% of tests) and identified actionable hits in 100%, 100%, and 78%, respectively. The median time to report was shorter for scFPM than CGP testing (7 vs. 19 days, p&amp;lt;0.0001; Figure 1B). CGP identified a median of 1 (0-5) druggable genetic targets. In 60% of the patients (12 out of 20) with complete work-up, we observed a substantial overlap between genetic and functional (within the top 10 drug hits) results. Conclusions Our results provide further evidence that either genetic-based or functional precision medicine can be efficiently and safely implemented in clinical routine. scFPM and CGP aid to identify a rationale for innovative treatment options in a heavily pretreated cohort. Treatment recommendations can be translated to clinical application in the majority of these patients.

'Days of Frustration': A Qualitative Study of Adolescents' Thoughts and Experiences of Schooling after Early Dropout.

School dropout increases the risk of unemployment, health problems, and disability benefits. Employing an ecological-developmental perspective, we analyzed the interviews of thirteen students from a peripheral Norwegian county, aiming to explore the possible influence of upbringing and schooling on dropout. The analysis revealed that dropout was associated with an unstable family situation, lack of structure in everyday life, unresolved complex learning difficulties, bullying, and a tough existence in a rented room. The participants conveyed a sense of defeat, frustration, and an absence of meaningful alternatives. However, two participants had actively chosen to discontinue their education; this was because they preferred work practice to allow them time to mature and re-orientate in relation to future educational and career choices. Their families and social networks contributed actively to the implementation of their future plans. The findings point to the importance of studying interventions that may prevent school dropout, and that address central factors in the process of school dropout, such as social support, academic achievement, and parental involvement.