Dropout Rates Research Articles

Is the Unified Protocol for Children Effective for the Transdiagnostic Treatment of Children’s Emotional Disorders? A Randomized Controlled Trial

The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders in Children (UP-C) aims to reduce internalizing symptoms in children by addressing fundamental mechanisms that underlie emotional disorders through a combination of cognitive, emotional, and behavioral strategies. This study, the largest randomized controlled trial (RCT) to date of UP-C efficacy and the first one conducted outside the U.S., assesses the efficacy of UP-C compared to an active control group. A total of 153 children (6–13 years old; 58.8% girls) with at least one emotional disorder as a primary diagnosis and their parents (88.2% mothers) were randomly assigned to either the UP-C condition (n = 77) or an active control condition (n = 76; receiving a psychoeducational intervention, the ABC of Emotions). Participants completed self-report questionnaires at pretreatment, midtreatment, posttreatment, and at the 3-month follow-up, assessing children’s internalizing symptoms and their interference with the child and family functioning. Clinicians assessed the severity of the child’s symptoms and monitored clinical improvement throughout the study. Descriptive statistics, comparative tests and Linear Mixed Models were used. Results showed greater satisfaction, lower dropout rates, and better adherence in the UP-C condition, with a reduced need for additional interventions posttreatment compared to the control group. The UP-C demonstrated significantly greater efficacy in reducing internalizing symptoms, with a large effect size, including anxiety and depression, as well as in reducing the interference and severity of child symptoms and in improving clinical gains compared to the control group. These findings provide strong support for UP-C as a feasible, acceptable, and effective treatment for childhood emotional disorders, reinforcing its status as an evidence-based treatment.

The Implementation of the Family Hope Program in Banggai Regency

The Family Hope Program (Indonesia: Program Keluarga Harapan / PKH) in Banggai Regency, a conditional cash transfer initiative aimed at improving the living standards of poor and vulnerable families in Indonesia, faces both successes and persistent challenges. This study aims to analyze the effectiveness of PKH's implementation, focusing on beneficiary identification, aid disbursement, and coordination among stakeholders. Employing a qualitative research approach, the study collected data through in-depth interviews, participant observation, and document analysis, following the Miles and Huberman (1994) model of data reduction, display, and conclusion verification. The findings reveal that while PKH has significantly enhanced access to education and healthcare, particularly increasing school attendance and reducing dropout rates, it faces ongoing issues such as data inaccuracies and coordination difficulties. These challenges, particularly in reaching marginalized communities in remote areas, underscore the need for improved data management and more tailored community engagement strategies. The study concludes that addressing these challenges is crucial for the program to fully realize its potential in alleviating poverty and promoting social welfare in Banggai Regency.

Performance of Deep Feed-Forward Neural Network Algorithm Based on Content-Based Filtering Approach

Background: Selecting a restaurant in a diverse city like Bandung can be challenging. This study leverages Twitter data and local restaurant information to develop an advanced recommendation system to improve decision-making. Objective: The system integrates content-based filtering (CBF) with deep feedforward neural network (DFF) classification to enhance the accuracy and relevance of restaurant recommendations. Methods: Data was sourced from Twitter and PergiKuliner, with restaurant-related tweets converted into rating values. The CBF combined Bag of Words (BoW) and cosine similarity, followed by DFF classification. SMOTE was applied during training to address data imbalance. Results: The initial evaluation of CBF showed a Mean Absolute Error (MAE) of 0.0614 and a Root Mean Square Error (RMSE) of 0.0934. The optimal DFF configuration in the first phase used two layers with 32/16 nodes, a dropout rate of 0.3, and a 20% test size. This setup achieved an accuracy of 81.08%, precision of 82.89%, recall of 76.93%, and f1-scores of 79.23%. In the second phase, the RMSprop optimizer improved accuracy to 81.30%, and tuning the learning rate to 0.0596 further increased accuracy to 89%, marking a 9.77% improvement. Conclusion: The research successfully developed a robust recommendation system, significantly improving restaurant recommendation accuracy in Bandung. The 9.77% accuracy increase highlights the importance of hyperparameter tuning. SMOTE also proved crucial in balancing the dataset, contributing to a well-rounded learning model. Future studies could explore additional contextual factors and experiment with recurrent or convolutional neural networks to enhance performance further.

Willingness to pay for a tobacco-free life: a contingent valuation assessment.

Tobacco, being an established risk factor for significant mortality and morbidity, causes over 7 million annual deaths globally. India is a country with over 270 million active tobacco users. On the one hand, although the willingness to quit amongst tobacco users is as high as 55.4%, the actual quit rates translate to less than 20%. Taking a cue from the economic principle of commitment and consistency, paying a nominal fee can serve as a form of public commitment and motivation to decrease the dropout rate amongst those committed to quitting. This study uses contingent valuation assessment to determine the willingness to pay (WTP) values for an effective tobacco cessation service across individuals with different socio-demographic characteristics. A cross-sectional observational study with multistage cluster sampling in Rajasthan was conducted to recruit 360 active tobacco users from 8 districts. Q-Q plots were used to determine the non-parametric distribution, and statistical differences between median WTP values were compared using Mann-Whitney U and Kruskal-Wallis H tests, considering a p-value less than 0.05 to be statistically significant. The majority of participants were males (57.78%) between the ages of 26 and 35 (40.28%), married (82.50%), and with children (65.83%), with an annual income between USD 7500-10,000 (23.61%) and graduate-level education (40.56%). Exclusive smokers formed the majority of the cohort (48.06%), and 50.83% were willing to quit as well as willing to pay for cessation. Median WTP values were maximum for males (USD 54.05; p<0.05), age group 26-35 years (USD 54.05; p<0.05), post-graduates (USD 81.08; p<0.05), and annual income above USD 20,000 (USD 121.62; p<0.05). Weak positive and significant correlations were observed between WTP and annual spending on tobacco, annual spending on health, and annual income. The study emphasizes the critical importance of early-age interventions for effective tobacco cessation prior to transitioning into dependency, resulting in reduced financial parity for availing health services. These are the first such WTP values from the Southeast Asian region, tailored for different population subsets, specifically for tobacco cessation. Our findings contribute to the growing body of evidence to support innovative approaches to enhance tobacco cessation efforts through financial commitments.

Unveiling the Motivations and Challenges: an Introspection into Women's Football in Hungary

The article evaluates the current state of hungarian women's football. It recognizes progress in the women's game while addressing persistent challenges like social perceptions and financial disparities. The research aims to comprehend the motivation of Hungarian adult female football and futsal players, exploring aspects such as the supportive environment and reasons for sustained engagement (N=175). Findings reveal the dual nature of women's football in Hungary, encompassing both competitive and recreational aspects. We delve into players' sentiments, thoughts of quitting, and future plans within the sport. The motivation analysis underscores the prevalence of intrinsic motivation rooted in genuine interest, with elite players exhibiting stronger extrinsic motivation. The study emphasizes football's positive impact and advocates for promoting physical activity and lifelong engagement. In conclusion, we calls for further development in women's football, addressing challenges and promoting recreational aspects. It advocates for strategies to reduce dropout rates and highlights football's potential for positive societal changes.

Patient-perceived factors on treatment satisfaction in early onset scoliosis treated surgically with a minimum of ten years

BackgroundThe prognosis of surgically treated subjects with early onset scoliosis (EOS) into adulthood has been lacking. We aimed to investigate the patients’ perspectives on satisfaction with surgical treatment.MethodologyWe included all surgical candidates with EOS who had undergone index spinal surgery for scoliosis correction between 2009 and 2013. The minimum duration of postoperative follow-up was 10 years at the time of survey completion. Three questionnaires were used in this study, comprising the revised Scoliosis Research Society questionnaire (SRS-22r), the Patient-Reported Outcomes Measurement Information System (PROMIS-29), and the World Health Organization Quality of Life (WHOQOL-BREF). Measures of treatment satisfaction were retrieved from SRS-22r.ResultsThere were 29 participants who completed the survey, and thereby included in the study (i.e., a response rate of 43% and a dropout rate of 6%). Amongst them, 14, 11, and 4 individuals received posterior spinal fusion (PSF), magnetically controlled growing rods (MCGR), and traditional growing rods, respectively. The average duration after the index spinal surgery was 12.6 ± 2.2 years. Our findings revealed that self-image (across all treatment groups), sleep disruption (only in PSF), and social aspects (in both PSF and MCGR) were significantly worse when compared to the normative values. According to the multivariable linear regression model (R-square = 0.690, p < .001), an increase in SRS-22r mental health (p = .008) and PROMIS-29 social participation scores (p = .004) corresponded to 0.511 and 0.055 points increases in satisfaction. Every unit increase in PROMIS-29 fatigue (p = .043) and WHOQOL-BREF physical domain scores (p = .007) was in conjunction with 0.019 and 0.040 points decreases in satisfaction. SRS-22r self-image (p = .056) and WHOQOL-BREF environmental domain scores (p = .076) were included in the model but did not reach statistical significance.ConclusionsTo improve the long term quality of life in surgical candidates with EOS, we demonstrated that mental health, social participation, fatigue, and physical health were significant factors associated with treatment satisfaction. Interestingly, demographic and radiographic parameters did not have a significant effect in our cohort.

Perspective: Clinical care of pedophilic individuals in Zurich, Switzerland.

Individuals with pedophilia are considered to have an elevated risk for child sexual abuse (CSA). Nevertheless, it is assumed that pedophilic sexual impulses can be controlled from acting out. To prevent CSA an outpatient treatment facility for people with pedophilia was founded in Zurich, Switzerland in 2021. The program focuses on the prevention of CSA and improvement of quality of life, incorporating empirically validated treatment principles, such as the Risk-Need-Responsivity (RNR) model and the Good Lives Model (GLM). Within the initial 24-month 142 individuals sought help, 46 individuals (mean age 36.0 ± 12.4 years) completed the assessment phase, two-thirds suffered from psychiatric comorbidities, and 67.4% reported sexual delinquency. The high drop-out rate was predominantly due to ongoing criminal proceedings, or other mental health conditions. Overall patients at this facility generally sought treatment voluntarily, leading to good treatment adherence, with severe self-harm being more prevalent than acute danger to others. A treatment approach focused solely on pedophilia is considered insufficient; psychiatric and psychosocial factors must also be addressed.

Effectiveness of a Customized Peritoneal Dialysis Training Program in Reducing Infection and Dropout Rates: Insights From a Singapore Hospital.

Introduction Peritoneal dialysis (PD) is an essential home-based treatment for end-stage kidney disease, known for enhancing patients' quality of life and being more cost-effective compared to hemodialysis. However, in Singapore, PD education lacks of standardization, with each unit adopting varied methods based on their own experiences and resources. To address this, our hospital developed a tailored four-day PD training program guided by the International Society for Peritoneal Dialysis guidelines, adapted to meet local needs and resource availability. Methodology This study employed a retrospective cohort design, including all incident adult patients aged 18 years and above who initiated PD at our hospital from September 2018 to July 2023. Data on PD dropout rates and PD-related infection rates, such as PD peritonitis and exit site infection rates, were obtained from electronic medical records. Results This study comprised 99 patients who began PD and completed their PD training program at our hospital between September 2018 and July 2023. Our tailored PD training program successfully reduced dropout rates and maintained infection rates within the International Society for Peritoneal Dialysis guidelines. Specifically, exit site infection rates fluctuated between 0.18 and 0.29 episodes per year, PD peritonitis rates ranged from 0.2 to 0.26 episodes per patient-year, and dropout rates significantly improved from 40% in 2019 to 7% in 2023 (OR = 0.45, 95% CI = 0.49 to 0.84, p = 0.010). Conclusions The tailored PD training program at our hospital effectively reduced PD-related infections and dropout rates among end-stage kidney disease patients. These findings suggest that structured, locally adapted training programs can substantially improve patient outcomes in PD.

Predicting Student Dropout Rates Using Supervised Machine Learning: Insights from the 2022 National Education Accessibility Survey in Somaliland

High student dropout rates are a critical issue in Somaliland, significantly impeding educational progress and socioeconomic development. This study leveraged data from the 2022 National Education Accessibility Survey (NEAS) to predict student dropout rates using supervised machine learning techniques. Various algorithms, including logistic regression (LR), probit regression (PR), naïve Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were employed to analyze the survey data. The analysis revealed school dropout rate of 12.67%. Key predictors of dropout included student’s grade, age, school type, household income, and type of housing. Logistic regression and probit regression models highlighted age and student’s grade as critical predictors, while naïve Bayes and random forest models underscored the significance of household income and housing type. Among the models, random forest demonstrated the highest accuracy at 95.00%, indicating its effectiveness in predicting dropout rates. The findings from this study provide valuable insights for educational policymakers and stakeholders in Somaliland. By identifying and understanding the key factors influencing dropout rates, targeted interventions can be designed to enhance student retention and improve educational outcomes. The dominant role of demographic and educational factors, particularly age and student’s grade, underscores the necessity for focused strategies to reduce dropout rates and promote inclusive education in Somaliland.

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

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

Efficacy of eHealth Versus In-Person Cognitive Behavioral Therapy for Insomnia: Systematic Review and Meta-Analysis of Equivalence.

Insomnia is a prevalent condition with significant health, societal, and economic impacts. Cognitive behavioral therapy for insomnia (CBTI) is recommended as the first-line treatment. With limited accessibility to in-person-delivered CBTI (ipCBTI), electronically delivered eHealth CBTI (eCBTI), ranging from telephone- and videoconference-delivered interventions to fully automated web-based programs and mobile apps, has emerged as an alternative. However, the relative efficacy of eCBTI compared to ipCBTI has not been conclusively determined. This study aims to test the comparability of eCBTI and ipCBTI through a systematic review and meta-analysis of equivalence based on randomized controlled trials directly comparing the 2 delivery formats. A comprehensive search across multiple databases was conducted, leading to the identification and analysis of 15 unique randomized head-to-head comparisons of ipCBTI and eCBTI. Data on sleep and nonsleep outcomes were extracted and subjected to both conventional meta-analytical methods and equivalence testing based on predetermined equivalence margins derived from previously suggested minimal important differences. Supplementary Bayesian analyses were conducted to determine the strength of the available evidence. The meta-analysis included 15 studies with a total of 1083 participants. Conventional comparisons generally favored ipCBTI. However, the effect sizes were small, and the 2 delivery formats were statistically significantly equivalent (P<.05) for most sleep and nonsleep outcomes. Additional within-group analyses showed that both formats led to statistically significant improvements (P<.05) in insomnia severity; sleep quality; and secondary outcomes such as fatigue, anxiety, and depression. Heterogeneity analyses highlighted the role of treatment duration and dropout rates as potential moderators of the differences in treatment efficacy. eCBTI and ipCBTI were found to be statistically significantly equivalent for treating insomnia for most examined outcomes, indicating eCBTI as a clinically relevant alternative to ipCBTI. This supports the expansion of eCBTI as a viable option to increase accessibility to effective insomnia treatment. Nonetheless, further research is needed to address the limitations noted, including the high risk of bias in some studies and the potential impact of treatment duration and dropout rates on efficacy. PROSPERO CRD42023390811; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=390811.

Glypred: Lysine Glycation Site Prediction via CCU-LightGBM-BiLSTM Framework with Multi-Head Attention Mechanism.

Glycation, a type of posttranslational modification, preferentially occurs on lysine and arginine residues, impairing protein functionality and altering characteristics. This process is linked to diseases such as Alzheimer's, diabetes, and atherosclerosis. Traditional wet lab experiments are time-consuming, whereas machine learning has significantly streamlined the prediction of protein glycation sites. Despite promising results, challenges remain, including data imbalance, feature redundancy, and suboptimal classifier performance. This research introduces Glypred, a lysine glycation site prediction model combining ClusterCentroids Undersampling (CCU), LightGBM, and bidirectional long short-term memory network (BiLSTM) methodologies, with an additional multihead attention mechanism integrated into the BiLSTM. To achieve this, the study undertakes several key steps: selecting diverse feature types to capture comprehensive protein information, employing a cluster-based undersampling strategy to balance the data set, using LightGBM for feature selection to enhance model performance, and implementing a bidirectional LSTM network for accurate classification. Together, these approaches ensure that Glypred effectively identifies glycation sites with high accuracy and robustness. For feature encoding, five distinct feature types─AAC, KMER, DR, PWAA, and EBGW─were selected to capture a broad spectrum of protein sequence and biological information. These encoded features were integrated and validated to ensure comprehensive protein information acquisition. To address the issue of highly imbalanced positive and negative samples, various undersampling algorithms, including random undersampling, NearMiss, edited nearest neighbor rule, and CCU, were evaluated. CCU was ultimately chosen to remove redundant nonglycated training data, establishing a balanced data set that enhances the model's accuracy and robustness. For feature selection, the LightGBM ensemble learning algorithm was employed to reduce feature dimensionality by identifying the most significant features. This approach accelerates model training, enhances generalization capabilities, and ensures good transferability of the model. Finally, a bidirectional long short-term memory network was used as the classifier, with a network structure designed to capture glycation modification site features from both forward and backward directions. To prevent overfitting, appropriate regularization parameters and dropout rates were introduced, achieving efficient classification. Experimental results show that Glypred achieved optimal performance. This model provides new insights for bioinformatics and encourages the application of similar strategies in other fields. A lysine glycation site prediction software tool was also developed using the PyQt5 library, offering researchers an auxiliary screening tool to reduce workload and improve efficiency. The software and data sets are available on GitHub: https://github.com/ZBYnb/Glypred.

The trajectory of body image dissatisfaction during pregnancy and postpartum and its relationship to Body-Mass-Index.

During pregnancy, women's bodies undergo rapid body weight and size changes within a relatively short period. Pregnancy may therefore, be associated with an increased vulnerability for developing body image dissatisfaction linked to adverse health outcomes for the mother (e.g., depression, eating disorders) and child (e.g., impaired self-regulation, childhood obesity). The present study aims to examine the prevalence and trajectories of body image dissatisfaction during pregnancy and postpartum and its relationship to pre-pregnancy BMI. This is the first study that investigates prevalence rates of body image dissatisfaction not only ante- but also postpartum, and that compares trajectories of women with normal weight and overweight. A prospective longitudinal design with a quantitative approach was applied. Healthy pregnant women (N = 136) answered paper-pencil or online questionnaires at four time points (18th-22nd and 33rd-37th week of gestation, 3 and 6 months postpartum). Body image dissatisfaction was assessed using the German version of the Body Shape Questionnaire (BSQ) and the Eating Disorder Examination Questionnaire (EDE-Q). Both questionnaires are considered reliable and valid measures of several aspects of body image, and the BSQ allows for calculating prevalence rates by providing cut-off values. Using not just one but two body image questionnaires, trajectories of body image dissatisfaction can be compared. Pre-pregnancy BMI was assessed retrospectively via self-reported weight and height. The proportion of women reporting elevated levels of body image dissatisfaction was 6.6% (n = 9) in the second trimester, 2.9% (n = 4) in the third trimester, 11.0% (n = 15) three months postpartum, and 10.3% (n = 14) six months postpartum. Repeated measures ANOVA revealed that body image dissatisfaction significantly decreased from pre-pregnancy to pregnancy, remained stable during pregnancy, and returned to pre-pregnancy levels three to six months postpartum. Mixed between-within ANOVA showed that the overweight/obese group reported significantly higher levels of body image dissatisfaction at each measurement point except during the third trimester than women in the normal weight group. Significant but small interaction effects between time and pre-pregnancy BMI were found. The results revealed that approximately every tenth woman is affected by body image dissatisfaction after childbirth. Women with a higher BMI level before pregnancy are particularly at risk of experiencing body image dissatisfaction. Healthcare providers should screen for body image dissatisfaction, in particular after childbirth, and inform affected women about possible adverse health outcomes and treatment options. Study limitations concern the drop-out rate of 51.4% and the retrospective and self-reported assessment of pre-pregnancy BMI. Future studies should include additional assessment points in the first trimester and more than six months postpartum and try to include a matched control group of non-pregnant women to compare prevalence rates and trajectory of body image dissatisfaction.

Evaluating the performance of Tunisian higher Institutes of Technological Studies (ISETs) using a stochastic frontier analysis

Benefiting from low repetition and dropout rates, as well as their excellent employability rate of their students, the Higher Institutes of Technological Studies (ISETs) have acquired a strategic position in the Tunisian higher education system. This paper aims to use the Stochastic Frontier Analysis (SFA) method to measure the efficiency of Tunisian Higher Institutes of Technological Studies (ISETs) and to determine the factors that cause performance differences. The results indicate that ISETs appear well managed, although some of them warrant a more detailed analysis (below-average efficiency). Also, it was found that the ISETs situated in the most industrialized part of the country, the Central-East, record highest scores of efficiency, while those in the South-East show more homogeneous efficiency. The results underscore the importance of focusing support and improvement efforts on ISETs located in less developed regions or those with lower efficiency levels. Moreover, the negative relationship between the age of institutions and their efficiency suggests that reforms to institutional practices may be necessary for older establishments. Finally, institutes that are located in one of the main cities will not necessarily be more efficient than the others. The findings presented in this paper have targeted and practical implications for the development of the ISET network in Tunisia.

Unveiling Dropout Patterns: A Python-Powered Analysis of Student Attrition in School Education

Student dropout rates pose a significant challenge in Gujarat, India, affecting both societal welfare and economic development. This study aims to diagnose and predict student dropout patterns using data analytics and machine learning (ML) algorithms. Extensive datasets from Gujarat's educational authorities are preprocessed with Python libraries such as Pandas and NumPy, ensuring quality and consistency. Exploratory data analysis (EDA) uncovers insights and trends, while feature engineering extracts relevant characteristics for ML models. The study implements ML algorithms Random Forest (RF), Decision Trees (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Machine(SVM) to construct predictive models. The findings reveal critical factors influencing dropout rates and assess model performance using model accuracy. This research provides novel insights into student dropout rates in Gujarat, offering a comprehensive, data-driven approach to address educational attrition and guide targeted intervention strategies and policy recommendations for improving educational outcomes in the region.

Classification of Dropout Rates in West Sumatra Using the Random Forest Algorithm with Synthetic Minority Oversampling Technique

Classification of Dropout Rates in West Sumatra Using the Random Forest Algorithm with Synthetic Minority Oversampling Technique

Cognitive training for schizophrenia: Do race and ethnicity matter?

Racial disparities in diagnosis, treatment, and outcome have been well-established for people with schizophrenia. While cognitive remediation treatments have been shown to produce mild to moderate improvements in cognition for people with schizophrenia, few studies have examined racial/ethnic differences in treatment response. This study employed a secondary analysis of data from two randomized, single-blind controlled trials (N = 119) investigating the efficacy of two forms of cognitive training, to explore potential racial/ethnic differences in targeted outcomes. Given the extant literature, we predicted that racial/ethnic minorities would (1) drop out of the study at higher rates, (2) display greater levels of functional and cognitive impairment prior to treatment, and (3) display lower levels of improvement in cognitive and functioning outcomes following treatment. Our study revealed largely negative findings: white vs. non-white groups showed similar treatment drop-out rates, similar levels of cognitive impairment and symptom severity at study baseline and showed similar responses to cognitive training, with the exception of working memory in which participants' racial/ethnic minority status predicted significantly greater improvement in response to cognitive training. These findings suggest that cognitive remediation treatments are effective at addressing cognitive deficits in racial/ethnic minorities and supports cognitive remediation as a treatment which may help address racial/ethnic disparities in cognition. Given the scant research literature, future analyses should look at race as a potential mediator of treatment in a variety of evidence-based psychosocial treatments.

Feasibility of individualized home exercise programs for patients with head and neck cancer-study protocol and first results of a multicentre single-arm intervention trial (OSHO #94).

Patients with head and neck cancer (PwHNC) benefit from targeted exercise interventions: symptom relief, compensation for dysfunction, improvement in quality of life (QoL). Data on acceptance physical interventions in PwHNC are rare. The 'OSHO #94' trial investigates the short- and medium-term effects of individualized home exercise in PwHNC on QoL, physical activity and functionality. The study includes a feasibility phase (proof of concept) in order to evaluate the acceptance. Here we present the study protocol as well as the feasibility results. This prospective, multicentre, single-arm intervention study includes PwHNC ≥18 years of age in aftercare or palliative care with stable remission under immunotherapy. The study opened in January 01, 2021, with estimated completion by December 31, 2024. The PwHNC receive an individualized home exercise program consisting of mobilization, coordination, strengthening and stretching exercises. This should be carried out at least three times a week over 12 weeks for 15 to 30 minutes, supplemented by aerobic training two to three times a week for 30 minutes (intervention). Once weekly telephone calls with a physiotherapist are performed. Subsequently, there is a 12-week follow-up (FU) without exercise specifications/contact. Outcomes are measured before and after the intervention and following the FU. Primary outcome of the feasibility phase (n = 25) was the determination of the dropout rate during the intervention with a termination cut off if more than 30% PwHNC withdrew premature. The primary outcome of the OSHO #94' trial (N = 53) is the change in global QoL score from pre- to post-intervention (EORTC QLQ-C30). Secondary outcomes include clinical and patient-reported measures, training details as well as functional diagnostic data (e.g. level of physical activity, training frequency, flexibility, fall risk and aerobic performance). 25 PwHNC were enrolled onto the feasibility cohort. Only16% (4/25 patients) did not complete the study. Therefore, recruitment of PwHNC was continued. The dropout rate was adjusted from 30% (N = 60) to 20% (N = 53, calculated sample size n = 42 PwHNC and 20% (n = 11) to dropout). Individualized home exercise programs in PwHNC in aftercare seem feasible. Consequently, the aim is now to evaluate the short and medium-term effects of individualized home exercise.

Vaccination Coverage and Predictors of Vaccination among Children Aged 12-23 Months in the Pastoralist Communities of Ethiopia: A Mixed Methods Design.

This study assessed vaccination coverage and its associated factors among children aged 12-23 months in pastoralist Ethiopia. It was conducted in three woredas of the Afar region using a community-based cross-sectional mixed methods design with quantitative and qualitative methods. A total of 413 mothers with children aged 12-23 months participated in the quantitative study via a simple random sampling technique. Logistic regression was used to identify factors associated with vaccination, and thematic analysis techniques were used for qualitative data. The percentage of patients who received full vaccination was 25%. Based on vaccination card observations, the dropout rate from Pentavalent-1 to Pentavalent-3 was found to be 2.9%. Logistic regression analysis revealed significant associations between mothers and caretakers with formal education, those who owned mobile phones, had antenatal care (ANC) visits, and birthed at a health facility with full vaccination. The overall proportion of full immunization is lower than the target set by the World Health Organization (WHO). The findings suggest that programs and policy makers should prioritize improving the access and enrolment of women and caretakers, promoting mobile phone ownership, and encouraging ANC visits and the promotion of health facility deliveries, as these are associated with higher rates of immunization.

Corrosion at Top-of-the-Line in High Pressure and Dense CO2 Environments

This study presents unique data on top-of-the-line corrosion (TLC) occurring in high-pressure environments where CO2 was in the gaseous, liquid, or supercritical state. While CO2 is traditionally in a gaseous phase, this form of degradation is referred to as TLC. In this study, similar phenomena with different mechanisms were observed in liquid CO2 and supercritical states all of which are referred to as TLC due to the location of specimens and ease of comprehension. Experiments were conducted to investigate the effect of CO2 partial pressure (ranging from 20 bar to 100 bar) with temperatures (30°C to 50°C) relating to different water condensation rates (0.001 mL/m2/s to 0.1 mL/m2/s). Uniform and localized TLC rates increased with a higher water condensation rate and surface temperature. As long as CO2 remained gaseous, its partial pressure (pCO2) showed a negligible influence on both uniform and localized TLC rates. At the highest gaseous CO2 content tested, the formation of a protective iron carbonate (FeCO3) layer decreased the TLC rate, with this effect being more pronounced at lower water condensation rates. The risk of localized corrosion for specimens exposed to this environment at high and medium water condensation rates remained an issue. In the dense phase CO2 environment, the difference in temperature between the bulk environment and the specimen’s surface caused a similar phenomenon to water condensation, termed water drop-out, which resulted in corrosion. The rate of water drop-out could not be measured experimentally or estimated theoretically but is a complex function of temperature, pCO2, and CO2 physical state. The interplay between high pCO2 and low pH of the dropped-out water led to elevated uniform and localized corrosion rates. The depth of localized corrosion, at the high and medium water drop-out conditions, reached its maximum at the surface temperature of ca. 45°C. At a lower surface temperature of ca. 25°C and a higher surface temperature of ca. 65°C, the maximum penetration rate was decreased due to slower kinetics of reactions and the formation of a more protective FeCO3 layer, respectively. The results presented in this study highlight the significant difference between corrosion rates, especially in the form of localized damage, in gaseous and dense-phase CO2 environments.