Reduce Attrition Rates Research Articles

On topological characterizations and computational analysis of benzenoid networks for drug discovery and development.

Topological indices are numerical invariants that provide key insights into the structural properties of molecular graphs and are crucial in predicting physio-chemical and biological activities. This paper applies established computational methodologies for analyzing benzenoid networks and their application to polycyclic aromatic hydrocarbons (PAHs) through degree-based topological indices computed via M-polynomial and NM-polynomial approaches. By examining tessellations, including linear chain, hexagonal, rhomboidal, and triangular configurations alongside their line graphs, this work highlights the influence of molecular topology on biological activity. Notably, the line graph of hexagonal tessellations resembling Kagome structures exhibits the highest potential bioactivity, revealing additional connectivity patterns that offer a structured framework for early-stage drug discovery and potentially enhance the understanding of molecular interactions. These findings underscore the value of topological indices in identifying key structural features, reducing attrition rates in drug development, and improving screening technologies, contributing to efficient drug design.

Factors influencing individuals to move away from careers as clinical radiographers.

Many factors can influence a radiographer to leave the clinical profession. The retention of radiographers in the clinical workforce is an issue for many countries including Ireland. This study aimed to explore factors influencing radiographers in Ireland to leave the clinical profession so that changes can be considered to reduce attrition from clinical workforce. The data collection consisted of online questionnaires and interviews to explore the job satisfaction of former radiographers who left the clinical radiography profession. The questionnaire, completed by 12 purposefully selected participants, included a validated job satisfaction tool, the job satisfaction questionnaire (JS-Q) for health workforce, answered twice to understand the participants job satisfaction levels during their time as clinical radiographers and in their current career. Six participants from the questionnaire group were also interviewed, answering eight questions further exploring their satisfaction in past and present roles. Analysis of interview transcripts revealed recurring themes linked to job satisfaction among participants. The JS-Q findings demonstrated a statistically significant disparity in satisfaction levels between participants' previous roles as clinical radiographers and their current occupations (p=0.0002) with participants reported higher job satisfaction in their current careers across all eight sub-themes. Data from the interviews reinforced the JS-Q findings, revealing that issues related to career advancement, lack of challenges, and inflexible work environments were driving individuals to leave clinical radiography. The findings align with previous research on staff retention strategies indicating that the attractiveness of a profession is influenced by the work environment, working circumstances, and possibilities for professional growth. Addressing these concerns may enhance clinical radiography retention rates. In particular, this study identified challenges for Irish clinical radiographers, showing marked differences in job satisfaction between their previous clinical roles and current careers. With the retention of clinical radiographers an ongoing challenge, the identification and exploration of factors influencing radiographers to leave the profession, must be considered and changes implemented in an effort to reduce attrition rate from clinical radiography in Ireland.

HELPING OR HINDERING EACH OTHER: SOME NUANCED PERSPECTIVES OF SOCIAL EMOTIONAL LEARNING

Social and emotional learning (SEL) is a broad and very popular topic in research. Within SEL, there are five interconnected components that must be understood to help each of us achieve a higher degree of self-awareness and improved interpersonal relationship with our fellow human beings. Some of the SEL behaviors discussed in this paper, though, are not always what they appear to be. These behaviors may be traumagenic in nature so an element of manipulation may be present unbeknownst to the other person in the conversation. Additionally, elements of social emotional learning can be used with doctoral candidates to help combat the disturbingly high attrition rates. In light of SEL, through an understanding of the AREA acronym, we faculty members, can also explore ways to help doctoral candidates succeed in their studies thereby helping to reduce attrition rates.

Predictive Analysis of Employee Turnover: A Comprehensive Model for Attrition Forecasting

Employee attrition has become a critical concern for organizations seeking to maintain a stable workforce and minimize turnover-related costs. This study aims to develop a predictive model to forecast employee attrition by analyzing key factors influencing turnover. The research investigates a range of variables, including push factors (e.g., job dissatisfaction, workplace conflict), pull factors (e.g., external job opportunities), organizational factors (e.g., leadership, culture), and external factors (e.g., economic conditions). The data is collected via a structured questionnaire through Google Forms, which captures responses related to these influencing factors.Using advanced machine learning techniques, this study evaluates multiple models, including logistic regression, decision trees, random forests, and support vector machines, to determine the most accurate model for predicting employee attrition. The evaluation is based on accuracy, precision, recall, and F1-score to ensure a robust prediction. The findings offer organizations valuable insights into the primary causes of employee turnover and provide a data- driven approach to proactively address these issues, reducing attrition rates and improving employee retention strategies. This research contributes to the existing body of knowledge by offering a comprehensive framework for predicting employee attrition, and its practical implications extend to human resource management, organizational development, and strategic planning. Keywords: Employee attrition, Turnover prediction, Predictive model, Push factors, Pull factors, Organizational factors, External factors, Machine learning, Logistic regression, Accuracy, Precision, Recall, F1-score, Employee retention, Human resource management, Workforce stability.

Drug-induced cholestasis (DIC) predictions based on in vitro inhibition of major bile acid clearance mechanisms.

Drug-induced cholestasis (DIC) is recognized as a major safety concern in drug development, as it represents one of the three types of drug-induced liver injury (DILI). Cholestasis is characterized by the disruption of bile flow, leading to intrahepatic accumulation of toxic bile acids. Bile acid regulation is a multifarious process, orchestrated by several hepatic mechanisms, namely sinusoidal uptake and efflux, canalicular secretion and intracellular metabolism. In the present study, we developed a prediction model of DIC using in vitro inhibition data for 47 marketed drugs on nine transporters and five enzymes known to regulate bile acid homeostasis. The resulting model was able to distinguish between drugs with or without DILI concern (p-value = 0.039) and demonstrated a satisfactory predictive performance, with the area under the precision-recall curve (PR AUC) measured at 0.91. Furthermore, we simplified the model considering only two processes, namely reversible inhibition of OATP1B1 and time-dependent inhibition of CYP3A4, which provided an enhanced performance (PR AUC = 0.95). Our study supports literature findings suggesting a contribution not only from a single process inhibition, but a rather synergistic effect of the key bile acid clearance processes in the development of cholestasis. The use of a quantitative model in the preclinical investigations of DIC is expected to reduce attrition rate in advanced development programs and guide the discovery and development of safe medicines.

A Novel Predictive Modeling for Student Attrition Utilizing Machine Learning and Sustainable Big Data Analytics

Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and big data aim to identify at-risk students early and intervene effectively. This study leverages big data and machine learning to identify key parameters influencing student dropout, develop a predictive model, and enable real-time monitoring and timely interventions by educational authorities. Two preliminary trials refined machine learning models, established evaluation standards, and optimized hyperparameters. These trials facilitated the systematic exploration of model performance and data quality assessment. Achieving close to 100% accuracy in dropout prediction, the study identifies academic performance as the primary influencer, with early-year subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control having a significant impact. The longitudinal effect of these subjects on attrition underscores the importance of early intervention. Proposed solutions include early engagement and support or restructuring courses to better accommodate novice learners, aiming to reduce attrition rates.

Sculpting the good surgeon or excising the bad one: How clinical teachers could perpetuate attrition in surgical residency programmes.

Attrition in surgical residencies remains a significant issue, with traditional research focusing mainly on individual and programme factors. This study explores the role of clinical teachers (CTs) in influencing attrition rates. CTs are essential in moulding residents' training, serving both as enablers of workplace learning and guardians of their medical fields. We employed a hermeneutic phenomenology framework to understand the sociocultural impacts on attrition. Data were collected through semi-structured interviews involving 19 CTs, 3 residents who left the programme and 2 who underwent remediation, following a six-step hermeneutic phenomenological analysis process. The entrenched 'good surgeon' narrative within the department demanded selflessness and total dedication, which CTs reinforced, thereby normalising a rigorous and challenging environment. This has led to attrition when residents fail to meet these challenges or choose to disengage from the system. We illustrated that CTs were pivotal in perpetuating these expectations, contributing significantly to resident attrition. CTs played a crucial role in resident attrition by enforcing a stringent cultural norm within surgical training programmes. Addressing this issue requires a visible change in CTs' role to foster a more supportive educational environment. Emphasising the beneficial aspects of the 'good surgeon' narrative and mitigating its adverse impacts is essential for reducing attrition rates and assisting all residents, including those facing challenges, in successfully completing their training.

Enhancement of Stress Granule Formation by a Chiral Compound Targeting G3BP1 via eIF2α Phosphorylation.

The chirality of a chemical differentiates it from its mirror-image counterpart. This unique property has significant implications in chemistry, biology, and drug discovery, where chiral chemicals display high selectivity and activity in achieving target specificity and reducing attrition rates in drug development. Stress granules (SGs) are dynamic assemblies of proteins and RNA that form in the cytoplasm of cells under stress conditions. Modulating their formation or disassembly could offer a novel approach to treating a wide range of diseases. This has led to significant interest in SGs as potential therapeutic targets. This study examined the NTF2-like domain of G3BP1 as a possible target for SG modulation. Molecular docking was used to simulate the interactions of compounds with the domain, and a potential candidate with a chiral structure was identified. The experiments showed that the compound induced the formation of SG-like granules. Importantly, the ability of this compound to modulate SG offers valuable insights into a new mechanism underlying the dynamics and promoting the assembly of SGs, and this new mechanism, in turn, holds potential for the development of drugs with diverse mechanisms of action and potentially synergistic effects.

Causes of moral distress among midwives: A scoping review.

Numerous studies have evidenced moral distress among midwives; however, to date no research synthesis on causes of moral distress among midwives has been conducted. A scoping review was carried out to identify, comprehensively map, and categorize possible causes of moral distress among midwives, and to identify knowledge gaps. Six data bases were searched using Boolean logic. To be included, studies had to (a) present empirical findings on (b) causes of moral distress (c) among midwives (d) in English, German, French, or Italian. We included a final set of 43 studies. The vast majority of studies came from high-income countries (83.7%) and used a qualitative approach (69.8%); 48.8% of the studies were published in the past 5years. Identified single reasons of moral distress were grouped into eight broader clusters, forming a coherent framework of reasons of moral distress: societal disregard, contemporary birth culture, resources, institutional characteristics, interprofessional relationships, interpersonal mistreatment of service users, defensive practice, and challenging care situations. These clusters mostly capture moral distress resulting from a conflict between external constraints and personal moral standards, with a smaller proportion also from an intraindividual conflict between multiple personal moral standards. Despite projected increases in demand for midwives, the midwifery workforce globally faces a crisis and is experiencing substantial strain. Moral distress further exacerbates the shortage of midwives, which negatively affects birth experiences and birth outcomes, ultimately rendering it a public health issue. Our findings offer points of leverage to better monitor and alleviate moral distress among midwives, contributing to reducing attrition rates and improving birth experiences and birth outcomes. Further research is essential to explore the issue of ecological moral distress, develop evidence-based interventions aimed at alleviating moral distress among midwives, and evaluate the effects of both individual and system-level interventions on midwives, intrapartum care, and service users' outcomes.

Psychometrics of the modified family-centered care assessment short version for childhood obesity

BackgroundIncorporating principles of family-centered care into pediatric weight management interventions can improve the effectiveness and quality of treatment and reduce attrition rates. To assess the family-centeredness of interventions, reliable, valid, and easy-to-administer scales are needed. The purpose of the study was to develop a shortened version of the modified Family Centered Care Assessment (mFCCA) and assess its psychometric properties.MethodsThe mFCCA, a scale to assess the family-centeredness of interventions for childhood obesity, was administered to families following the Connect for Health randomized control trial evaluating the effectiveness of a primary care-based pediatric weight management intervention. We iteratively removed items from the mFCCA and used Rasch modeling to examine the reliability and validity of the shortened scale.ResultsWe included data from 318 parents and the exploratory factor analysis showed the presence of a single factor. The results of the Rasch modeling demonstrated acceptable internal consistency of the scale (0.7) and strong validity as evidenced by the overall model fit and range of item difficulty. Following the psychometric analyses, we reduced the number of items from 24 to 8 items.ConclusionThe mFCCA short version demonstrates good psychometrics and can be used to evaluate the family-centeredness of childhood obesity interventions with reduced participant burden, thereby improving outcomes for children with obesity.Trial registrationClinicaltrials.gov NCT02124460 registered on April 24, 2014.

Framework Development for Reducing Attrition in Digital Dietary Interventions: Systematic Review and Thematic Synthesis.

Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes. This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities. We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis. Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective. High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions. PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.

Development of an in vitro model of the neurovascular unit for BBB permeability-linked neuroactivity screening

Many potential neurotherapeutic agents fail in the later stages during development due to insufficient blood-brain barrier (BBB) permeability or neurotoxic effects. To address this, we developed an in vitro model incorporating the neurovascular unit (NVU) — astrocytes, pericytes, neurons, and brain microvessel endothelial cells — designed to simulate the in vivo BBB and improve early drug screening. This model uses a direct contact triculture system enhanced by integrating SH-SY5Y neuron-like cells, enabling the study of permeability-linked neuronal responses. Our results show that this expanded NVU model, employing a Transwell® system, enhances the BBB’s restrictive properties and neuronal viability, potentially due to improved cell-cell signaling. Additionally, the model demonstrated increased efflux transporter expression, providing a more physiologically relevant assessment of neuroactivity in relation to BBB permeability. This innovative NVU model offers a predictive and robust tool for evaluating neurotherapeutic agents, facilitating the prioritization of candidates in large compound libraries and potentially reducing attrition rates in drug development. It represents a significant advancement in the methodology for early-stage neurotherapeutic screening, aligning in vitro findings more closely with in vivo responses.

Undergraduate dissertation problems: an aggregate cross-sectional analysis from five Latin American universities

ABSTRACT Latin America may have higher attrition rates than previously assumed. Many students in Latin America do not graduate on time because they cannot complete the traditional undergraduate dissertation. This paper examines the factors contributing to delayed graduation for a context-specific variable: dissertation problems. We designed a questionnaire survey based on multi-item scales and surveyed 480 university students from Argentina, Bolivia, Chile, Colombia, and Mexico. Aggregate-cross-sectional data and multilevel structural equation modeling show that institutional assistance and supervision quality [personal problems] reduce [increase] dissertation problems. These results may enable university authorities to identify undergraduate students with dissertation problems and thus improve their strategies for reducing attrition rates.

Psychometrics of the modified Family-Centered Care Assessment short version for childhood obesity.

Incorporating principles of family-centered care into pediatric weight management interventions can improve the effectiveness and quality of treatment and reduce attrition rates. To assess the family-centeredness of interventions, reliable, valid, and easy-to-administer scales are needed. The purpose of the study was to develop a shortened version of the modifed Family Centered Care Assessment (mFCCA) and assess its psychometric properties. The mFCCA, a scale to assess the family-centeredness of interventions for childhood obesity, was administered to families following the Connect for Health randomized control trial evaluating the effectiveness of a primary care-based pediatric weight management intervention. We iteratively removed items from the mFCCA and used Rasch modeling to examine the reliability and validity of the shortened scale. We included data from 318 parents and the exploratory factor analysis showed the presence of a single factor. The results of the Rasch modeling demonstrated acceptable internal consistency of the scale (0.7) and strong validity as evidenced by the overall model fit and range of item difficulty. Following the psychometric analyses, we reduced the number of items from 24 to 8 items. The mFCCA short version demonstrates good psychometrics and can be used to evaluate the family-centeredness of childhood obesity interventions with reduced participant burden, thereby improving outcomes for children with obesity. Clinicaltrials.gov NCT02124460 registered on April 24, 2014.

Preparedness of Occupational Therapy Academics to Support Students Experiencing Psychosis: A Qualitative Study

Students experiencing psychosis face increased difficulties within the higher education environment with support from academic staff being identified as one of the most significant factors in reducing attrition rates. Focus groups and interviews were used to consider the experiences of eight occupational therapy academics. Thematic analysis identified themes relating to the how of supporting a student experiencing psychosis; the challenges of supporting a student with psychosis; and mechanisms of support. Despite a small sample size, this study highlighted important considerations relating to the need for additional support and resources for academics to equip them to effectively provide student support.

Employee Attrition Prediction using Machine Learning Models: A Review Paper

Employee attrition refers to the decrease in staff numbers within an organization due to various reasons. As it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized it as a significant concern. To address this issue, organizations are increasingly turning to machine-learning approaches to forecast employee attrition rates. This topic has gained significant attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims to fill this gap by providing an overview of research conducted on applying machine learning to predict employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted, summarized, and classified. Most studies agree on conducting comparative experiments with multiple predictive models to determine the most effective one.From this literature survey, the RF algorithm and XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms. Additionally, the application of deep learning to employee attrition prediction issues also shows promise. While there are discrepancies in the datasets used in previous studies, it is notable that the dataset provided by IBM is the most widely utilized. This study serves as a concise review for new researchers, facilitating their understanding of the primary techniques employed in predicting employee attrition and highlighting recent research trends in this field. Furthermore, it provides organizations with insight into the prominent factors affecting employee attrition, as identified by studies, enabling them to implement solutions aimed at reducing attrition rates.

STUDENT PERSISTENCE IN ONLINE LEARNING: A LITERATURE REVIEW

The advantages of online learning have allowed learners to join courses that help them conveniently improve their knowledge and skills. One of the challenges facing online programs, however, is to retain students and address the issue of high dropout rates. This article reviews literature to determine factors influencing student persistence in online programs and explores solutions to reduce attrition rates. Ninety articles in peer-reviewed journals published between 2000 and 2022 were examined and included in the literature. The selection criteria consist of topic relevance, studies having empirical data and year of publication. Additional procedures involve searching databases, screening abstracts, analyzing full texts, and synthesizing. Factors contributing to student persistence in online learning include internal factors (i.e. motivation, satisfaction, and self-efficacy), external factors (i.e. financial aid, peer and family support), and student skills (i.e. time management and self-regulation skills). Several viable solutions are providing orientation programs, creating collaborative learning environments and enhancing faculty support. This critical review creates a foundation for further research on the issue of student retention in online programs.

The importance of improving cognitive flexibility in adolescents with anorexia nervosa: The case for cognitive remediation therapy.

Inefficiencies in executive functioning (EF), more specifically cognitive flexibility and an overly detailed processing style, are frequently observed in individuals with Anorexia Nervosa (AN) and have been identified as potential targets in treatment. Cognitive Remediation Therapy (CRT) is an adjunctive treatment approach specifically designed to have a positive impact on EF. Mainly evaluated in adults, CRT has been criticized for its perceived ineffectiveness in promoting weight restoration or directly reducing eating disorder symptoms. We argue that we need to refocus our conceptual framework for using CRT as an adjunctive treatment and specifically explore its potential benefit in adolescents. Adolescence is a critical window for EF development during which CRT has the potential to have the most impact. While it may not specifically ameliorate eating disorder symptoms and directly improve weight gain, CRT may mitigate the impact of malnutrition on adolescent brain development, reduce attrition rates in treatment, and improve cognitive flexibility and (indirectly) other maintaining factors, thereby improving global functioning. More research needs to be done to understand the development of EF in adolescents with AN and how best to employ CRT as an adjunctive treatment to support development and target maintaining factors. The current article broadly reviews findings on executive functioning inefficiencies in adolescents with AN and discusses the purpose and role of CRT in treating AN. Finally, we highlight key critiques of using CRT and pose questions for future research. Treatments targeting executive functioning in adolescents with AN are limited. We need to better understand how CRT can benefit adolescents in treatment. Increasing treatment options, including adjunctive treatments, is necessary to reduce the long-term impact of AN.

Unmasking impostor phenomenon: a study of nurses in specialist roles.

This cross-sectional study aimed to explore the incidence of impostor phenomenon among 79 nurses in specialist roles in a single NHS trust in England, using an online anonymous questionnaire that included the Clance Imposter Phenomenon Scale and free-text responses. Results indicated a high prevalence of impostor phenomenon, with no specific variables predicting its occurrence. Four main themes emerged: self-doubt, role expectations, fear of exposure as an impostor, and factors leading to burnout. The study highlights the need for targeted support and training for this cohort, particularly as impostor phenomenon was found to be prevalent during career transitions and was not limited by gender. The findings have implications for nursing management and education, offering the opportunity to develop specific support mechanisms to alleviate impostor phenomenon and potentially reduce attrition rates in specialist nursing roles.

Designing Multimedia Instructional Materials using Gagne’s Framework in Promoting Adult Learners’ Asynchronous Learning Experiences

This research addresses the challenges faced in Distance Education (DE) by investigating the impact of carefully designed multimedia instructional materials in creating a conducive asynchronous learning experience for adult learners. With the increasing integration of digital technologies in higher education, especially in DE, it becomes imperative to assess and optimize the various teaching tools available to ensure effective educational delivery. The unique challenge in DE is that adult learners often study in isolation, with limited instruction, and at a distance from traditional classroom activities. To mitigate these challenges, multimedia instructional materials are recognized for their potential to enhance the learning experience. This study delves into how the design of instructional materials, supported by technology, contributes to improving the asynchronous learning experience digitally and subsequently the learning outcomes. Well-designed instructional materials have been identified as crucial determinants of success, emphasizing the need for a learner-supported approach employing carefully selected instructional design. It is suggested that institutions offering DE prioritize the creation of high-quality multimedia instructional materials rooted in suitable learning theories, in supporting adult learners in DE to enhance learner satisfaction, improve academic performance, and reduce attrition rates. Further exploration and evaluation of this instructional design framework are certainly called recommended for a testament enhanced credibility for the said framework.