Attrition Model Research Articles

Low psychological resilience and physical fitness predict attrition from US Marine Corps Officer Candidate School training

ABSTRACT The objective is to examine the predictors of attrition in male and female candidates undergoing a 10-week early career military training program. 1006 candidates (79.5% male, 24.7 ± 3.2 years) consented to participating in a larger study examining predictors of injury during US Marine Corps Officer Candidates School (OCS). Participants completed a blood draw, demographic and psychological characteristics questionnaires, and two fitness tests. Participants were then grouped based on successful completion of OCS or not. Associations between potential predictors and attrition were analyzed using simple logistic regression analyses, followed by a backward stepwise elimination method. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to determine the accuracy of the attrition prediction model. 260 candidates (25.8%) attritted over the 10-week training, with the highest number of discharges during week 5. Musculoskeletal injury (MSKI) was the most common cause of attrition (30%), followed by non-MSKI medical (21.5%), and volitional withdrawals (19.6%). Sex, body mass index (BMI), resilience, initial physical fitness test score, combat fitness test (CFT) score, and prior military service were all significantly associated with attrition from OCS (all p < .05). The final prediction model of attrition included CFT score (p = .027) and resilience (p = .018). Multiple demographic, psychological, and fitness characteristics are associated with attrition from an early career military training course (OCS) and may be utilized as part of early screening procedures to identify and provide guidance for individuals at risk for not completing OCS.

Integrated attrition model of mechanical-thermal-reaction for CaCO3/CaO thermochemical energy storage

Fluidized bed reactors have become a pivotal trend in the future development of thermochemical energy storage. However, high temperatures and chemical reactions exacerbate particle attrition in fluidized bed reactors, affecting particle cyclic stability and reducing energy storage efficiency. This study conducted experiments under three different temperature conditions to compare and investigate the attrition mechanisms of CaCO3/CaO particles. The contributions of mechanical forces from collisions, thermal stress due to uneven cooling and heating, and chemical stress from cyclic reactions to particle attrition are analyzed. The edge effects caused by sphericity dominate the attrition behavior during the initial period of fluidization. High-temperature thermal stress significantly weakens the attrition resistance of the particles, while repeated chemical cycling degrades the internal skeletal structure of the particles, lowering the fracture threshold. Based on fitting experimental data, a comprehensive numerical model for predicting particle attrition has been developed and improved by incorporating factors such as edge effects from sphericity, thermally induced stress, and chemically driven fragmentation. Through validation, the model effectively predicts particle attrition behavior in thermochemical storage process, providing a simulation tool for in-depth research on particle stability in thermochemical energy storage field.

Analysis of HZSM‐5 molecular sieve particles attrition behavior under fluidized conditions

AbstractThe attrition behavior of HZSM‐5 zeolite catalyst particles at room temperature was investigated in a laboratory‐scale fluidized bed. The effects of three fluidization conditions on particle attrition were investigated, and a new attrition model was proposed. The results demonstrate that the attrition rate is inversely proportional to the initial particle size and proportional to the apparent gas velocity. After increasing to 80 μm and .3 m/s respectively, they are no longer the main factor affecting attrition. The effect of bed pressure on attrition rate is nonlinear, and the lowest attrition rate is obtained when the diameter‐height ratio is 1:1. Unsteady attrition stage can be divided into initial stage and deceleration stage. Surface delamination dominates particle attrition throughout the whole process, and bulk fracture is the dominant mechanism only in the deceleration stage. Based on the Gwyn equation, a new attrition model in the form of cubic polynomial is established with the ratio of total attrition rate to unstable attrition rate P as a parameter. The model has high accuracy and repeatability and is suitable for various fluidization conditions. It can effectively describe the attrition process and change rule of particles and reasonably predict the fluidization attrition rate of particles.

Revisit Attraction–Selection–Attrition Model for Teacher Retention in International Schools

&lt;p style="text-align:justify"&gt;The Attraction-Selection-Attrition (ASA) model is a prominent framework for supporting employee retention, stating that organisations attract, select, and retain people who share their values. However, the ASA model only extends to the end of the recruitment stage and lacks clarity on how to assist newcomers in the assimilation process when they first join the organisation. This research proposed a refinement of the ASA paradigm by incorporating the assimilation process of new hires into the new school culture and environment. This study employed a qualitative research approach by interviewing ten participants about the retention process from high teacher retention international schools in Malaysia. Thematic data analysis revealed a new paradigm, 'Attraction-Selection-Onboarding-Retention (ASOR), ' designed to increase teacher retention in international schools. The ASOR model could assist school administrators and human resource managers working in a related setting in properly engaging the workforce to increase teacher retention. This would benefit school sustainability, performance and the local community's economy.&lt;/p&gt;

Developing a model describing voluntary residency attrition: a qualitative study

BackgroundMany factors influencing residency attrition are identified in the literature, but what role these factors play and how they influence each other remains unclear. Understanding more about the interaction between these factors can provide background to put the available evidence into perspective and provide tools to reduce attrition. The aim of this study was therefore to develop a model that describes voluntary residency attrition.MethodsSemi-structured interviews were held with a convenient sample of orthopaedic surgery residents in the Netherlands who dropped out of training between 2000 and 2018. Transcripts were analysed using a constructivist grounded theory approach. Concepts and themes were identified by iterative constant comparison.ResultsSeventeen interviews with former residents were analysed and showed that reasons for voluntary attrition were different for each individual and often a result of a cumulative effect. Individual expectations and needs determine residents’ experiences with the content of the profession, the professional culture and the learning climate. Personal factors like previous clinical experiences, personal circumstances and personal characteristics influence expectations and needs. Specific aspects of the residency programme contributing to attrition were type of patient care, required skills for the profession, work-life balance and interpersonal interaction.ConclusionsThis study provides a model for voluntary resident attrition showing the factors involved and how they interact. This model places previous research into perspective, gives implications for practice on the (im)possibilities of preventing attrition and opens possibilities for further research into resident attrition.

Recent Advances in Group-Based Trajectory Modeling for Clinical Research.

Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups.

Dropout intent of students with disabilities

We examine the mechanisms explaining the dropout intentions of students with disabilities by integrating Tinto’s model of student integration, the student attrition model, the composite persistence model, and insights from social stratification research. The resulting theoretical model posits that not only students’ academic and social integration, but also their private resources (financial, home learning, and personal resources) are crucial for academic success. Analysing data from a 2020 Germany-wide student survey, we find that students with disabilities are substantially more likely to intend to drop out of higher education than students without disabilities. Linear regressions and Kitagawa-Oaxaca-Blinder decompositions show that their lower academic integration and fewer personal resources are most relevant for explaining this difference, while their lower social integration, home learning, and financial resources play subordinate roles. Further analyses reveal that dropout intent is highest among students with psychic disabilities, followed by students with learning disabilities and students with physical disabilities. Regarding all three disability groups, less academic integration and fewer personal resources are most relevant for explaining their higher dropout intent (compared to students without disabilities). However, the disability groups differ regarding the importance of the different explanatory factors. Overall, our results highlight the importance of considering both students’ integration into higher education and their private resources for understanding student-group-specific dropout intent.

Applying the Attrition Model to the Medical System: A Critique of the Current Resistance by the Naturopathic Profession in Canada to the Dominant Paradigm

The premise and attrition model of resistance proposed by some in the anti-psychiatry movement are relevant to all subspecialties of medicine. Themes of monopolization, capitalization and marginalization harmfully affect the delivery of health care across all domains and minimize systemic and structural contributors to health and disease. Naturopathic medicine offers promise to effectively support individuals and communities in navigating modern obstacles to good health but may, in practice, be ina vertently reinforcing the dominant paradigm. The attrition model offers guidance for a steady, strategic resistance instead.

Why We Left: A Qualitative Investigation of Former Physician Assistant Faculty.

In an era of rapid professional growth, retaining a workforce of career physician assistant (PA) educators is crucial; yet, PA education has historically struggled with faculty retention. The purpose of this study was to examine the lived experiences of PAs who have left academia in order to better understand PA faculty attrition. Purposeful sampling was used to identify PAs who had recently left academic positions, with recruitment continuing until thematic saturation was reached. A total of 18 semi-structured interviews were conducted via phone or email, and a thematic qualitative analysis of the transcripts was completed. Ineffective leadership, unsustainable workloads, inadequate mentorship or training, erroneous expectations of academic work, and a "pull" back to clinical practice emerged as dominant themes in participants' decisions to leave academia. Ineffective leadership was an issue at both the program and the institutional level, resulting in feelings of inadequate institutional support. The availability of clinical jobs facilitated attrition decisions by providing an easily accessible exit from academia. This research provides a model for understanding PA faculty attrition and has implications for retention. Effective program leadership that supports new faculty development, creates sustainable workloads, and advocates for the program within the institution has a significant role in faculty retention. Leadership development should be a priority for the profession and is vital to securing a PA education workforce. A limitation of this study is that the data were collected prepandemic, so the impact of recent cultural and institutional changes is unknown.

Experimental and numerical study on characteristics and mechanism of particles attrition in fluidized bed

Particle attrition is commonly existed in gas-solid two-phase flow process. A novel CFD-DEM particle attrition model is developed, a series of experiments/simulation studies are performed to quantitatively investigate the attrition mechanism of particles in fluidized beds. The particle attrition process could generally be divided into unstable attrition stage and stable attrition stage, while the results show that unsteady attrition stage should be subdivided into high-speed attrition phase and low-speed attrition phase. Both wear and fragmentation become more intense with increasing height-diameter ratio and superficial gas velocity. The wear ratio decreases with increasing particle size, while the fragmentation ratio firstly decreases and then increases. Particle attrition mechanism also presents spatial ununiformity, particles reaching the bottom are worn most significantly, while the proportion of fragmentation based on this study for the sidewall areas, the bottom area and the center area is about 69%, 22% and 9% respectively.

The role of teacher selection criteria and preparation on teacher self-efficacy, satisfaction, and commitment: an analysis of Kazakhstani TALIS data

ABSTRACT In Kazakhstan, an increasing school student population, low rates of graduates entering the profession, and high novice teacher attrition have made the attraction and retention of quality teachers a critical issue. To investigate this problem, the authors draw on the 2018 TALIS dataset involving a survey of 5201 Kazakhstani teachers from 275 participating schools. The study, framed by Chapman’s teacher attrition model, uses a nested data design. As an outcome variable, the authors make use of a corollary of attrition, the self-reported career commitment variable in the Kazakhstani TALIS dataset. The analysis suggests that teachers who enter the profession with enhanced altruistic tendencies have improved self-efficacy and job satisfaction. Further, teaching-as-a-first-career-choice appears to drive both job satisfaction and career commitment, while learning to manage behaviour and mixed-ability settings also appears to drive improved long-term career commitment. Implications for teacher education and research in other Central Asian and post-Soviet countries are offered.

Modeling of real combat operations

This paper discusses the model of an offensive air-ground combat operation, a product of a specific modeling - operational planning and targeting process. The modeling was performed using approximate methods of numerical integration based on Lanchester's law of combat. Operational factors and their implications for outcome are used for modeling a special but still current, offensive air-ground campaign/operation "Desert Storm". The model considers relevant factors, such as combat capabilities, the number of forces and consumption of resources and forces attrition in combat, in order to enable planning and prediction of the winning side in battle. The optimal solution of the problem as a useful strategic tool, based on the attrition model of combat has a practical benefit for making the right management decisions in operational planning process. This approach corresponds to project management, considering the same operational factors and the ability to manage them.

Development of a Predictive Model of Student Attrition Rate

Enrollment in courses is a key performance indicator in educational systems for maintaining academic and financial viability. Today, a lot of factors, comprising demographic and individual features like age, gender, academic background, financial capabilities, and academic degree of choice, contribute to the attrition rates of students at various higher education institutions. In this study, we developed prediction models for students' attrition rate in pursuing a computer science degree as well as those who have a high chance of dropping out before graduation using machine learning methodologies. This approach can assist higher education institutions in creating effective interventions to lower attrition rates and raise the likelihood that students will succeed academically. Student data from 2015 to 2022 were collected from the Federal University Lokoja (FUL), Nigeria. The data was preprocessed using existing WEKA machine learning libraries where our data was converted into attribute-related file form (ARFF). Further, the resampling techniques were used to partition the data into the training set and testing set, and correlation-based feature selection was extracted and used to develop the students' attrition model to identify the students' risk of attrition. Random Forest and decision tree machine learning algorithms were used to predict students' attrition. The results showed that Random Forest has 79.45% accuracy while the accuracy of Random tree stood at 78.09%. This is an improvement over previous results, where an accuracy of 66.14%. and 57.48% were recorded for random forest and Random tree respectively. This improvement was because of the techniques demonstrated in this study. It is recommended that applying techniques to the classification model will improve the performance of the model.

Identifying academically vulnerable learners in first-year science programmes at a South African higher-education institution

The Admission Point Score (APS) is used by most South African universities to identify a university programme in which a learner is likely to succeed. While the APS appears helpful to gauge the aptitude of a learner and predict their success, the reality is that between 2008 and 2015 almost 50% of learners who made the required APS for a Science programme failed to complete the requirements for that programme. This paper delineates and diagnoses learner vulnerability, using a learner attrition model, for early intervention and as an alternative to using the APS. The analysis shows that various predictive models achieve higher accuracy to predict learner vulnerability, by incorporating factors of the learner attrition model, rather than just using the APS score. This paper argues for a more complex view of predicting learner vulnerability for early interventions by incorporating the learner's background, individual characteristics, and schooling data. It does not agree with the aggregation of National Senior Certificate (NSC) subjects into APS scores since this normalises the complexity of the subtle relations between the schooling system, learner attrition, and pre-schooling pedagogical dynamics. This paper points to a more nuanced view of predicting learner vulnerability.

Predictive model of employee attrition based on stacking ensemble learning

Since human resource is the most important resource of a company, employee attrition is an important agenda from the company's point of view. However, employee attrition occurs due to various reasons, and it is difficult for the HR manager or the leader of each department to know these signs in advance. Employee attrition results in considerable burdens and losses of the organization due to a variety of reasons such as interruption of ongoing tasks, cost of employee re-employment and retraining, and risk of leaking core technologies and know-hows. Therefore, in this study, we propose a model for predicting employee attrition so that we can take measures for talent management which in the past, has been carried out ex post. In this study, a predictive model was constructed based on 30 variables - that affect employee attrition - from the 'IBM HR Analytics Employee Attrition & Performance data', which consists of 1,470 records. To this end, a total of eight predictive models, including Logistic Regression, Random Forest, XGBoost, SVM, Artificial Neural Network model and ensemble model, were built and their performance was evaluated. In addition, when the impact of variables on employee attrition was analyzed, variables such as environmental satisfaction, overtime work, and relationship satisfaction were found to be the biggest contributors.

Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions

Recently, students dropping out of school at the tertiary level without prior notice or permission has intrigued deep concern among academic authorities, instructors, and counsellors. It has therefore become necessary to understand factors that lead to high attrition rates among learners and identify at-risk students for urgent academic counselling. In providing a proactive response to learner attrition, the study deployed a machine learning algorithm with high model accuracy to predict students’ drop-out rates and identify dominant attributes that affect learner attrition and retention. An attrition model was built and validated among support vector machine, decision tree, multilayer perceptron, and random forest algorithms. The machine learning algorithms were tested for accuracy, precision, recall, F-measure, and ROC using the 10-fold and the 5-fold comparative cross-validation techniques. In addition to the cross-validation technique, the chi-square feature selection mechanism was implemented to understand the algorithms’ training time and accuracy. The random forest emerged as the best-performing algorithm, with an accuracy of 70.98% and 69.74% for the 10-fold and the 5-fold cross-validation implementations, respectively.

Predicting Student Attrition in Higher Education through the Determinants of Learning Progress: A Structural Equation Modelling Approach

Higher education policies are designed to facilitate students’ learning progression and academic success. Following Tinto’s integration theory and Bean’s attrition model, this study proposes a research model to investigate whether students prone to attrition can be pre-emptively identified through self-evaluating academic factors contributing to their learning progress. Theoretically, the learning progress is identified with student success, represented by factors amenable to intervention including the interaction with peers and instructors, teaching effectiveness, exam scores, absenteeism, students’ effort, and academic course-related variables. An exploratory and confirmatory factor analysis of 530 undergraduate students revealed that the indicators of learning progress in such students were channeled into two constructs. The results indicated that the teacher effectiveness and learning materials contributed most to the learning progress. Structural equation modelling revealed that the learning progress variables have a significant impact on students’ attrition status. A multi-group analysis confirmed the academic semesters to be a moderator in the mediating effects of the students’ grade point average (GPA). This model functions as a framework to design a student-oriented learning system promoting students’ learning experience and academic success.

Times are changing, but has natural mortality? Estimation of mortality rates for tropical tunas in the western and central Pacific Ocean

A variety of density-dependent and -independent processes have been proposed to influence natural mortality rates, potentially leading to variation through time. Processes of natural mortalities are rarely directly observed, making estimation of natural mortality rates difficult. Mark-recapture data allow estimation of total mortality rates, which can be separated into natural and fishing mortality with information on rates of tag reporting, tag shedding and tag-induced mortality. We fitted attrition models and length-based Brownie models to four decades of mark-recapture data from skipjack and yellowfin tuna in the Western and Central Pacific Ocean, a period representing a sustained expansion of associated fisheries in the region as well as rapid changes to the marine environment. The modelled dataset included c. 250,000 skipjack and 100,000 yellowfin tag releases, with 45,000 and 17,000 recoveries of skipjack and yellowfin respectively, released from 1977 to 2017. Increases in fishing mortality were detected over this time for both skipjack and yellowfin, with evidence of temporal changes in selectivity for yellowfin. Estimates of natural mortality were highest for the smallest size class and generally lower for larger sizes, though there was large uncertainty in the largest size groups due to lower sample sizes of tagged fish. There was no clear evidence of temporal changes in natural mortality rates for either species, though there was some evidence of changes in natural mortality for the smaller yellowfin size classes (< 61 cm). However, there was likely insufficient statistical power to test for plausible changes in natural mortality rates for yellowfin due to low precision of estimates during the earlier years of the tag dataset.

Attrition and associated factors among children living with HIV at a tertiary hospital in Eritrea: a retrospective cohort analysis

BackgroundReducing attrition in paediatric HIV-positive patients using combined antiretroviral therapy (cART) programmes in sub-Saharan Africa is a challenge. This study explored the rates and predictors of attrition in children started...

Machine learning methods to predict attrition in a population-based cohort of very preterm infants

The timely identification of cohort participants at higher risk for attrition is important to earlier interventions and efficient use of research resources. Machine learning may have advantages over the conventional approaches to improve discrimination by analysing complex interactions among predictors. We developed predictive models of attrition applying a conventional regression model and different machine learning methods. A total of 542 very preterm (< 32 gestational weeks) infants born in Portugal as part of the European Effective Perinatal Intensive Care in Europe (EPICE) cohort were included. We tested a model with a fixed number of predictors (Baseline) and a second with a dynamic number of variables added from each follow-up (Incremental). Eight classification methods were applied: AdaBoost, Artificial Neural Networks, Functional Trees, J48, J48Consolidated, K-Nearest Neighbours, Random Forest and Logistic Regression. Performance was compared using AUC- PR (Area Under the Curve—Precision Recall), Accuracy, Sensitivity and F-measure. Attrition at the four follow-ups were, respectively: 16%, 25%, 13% and 17%. Both models demonstrated good predictive performance, AUC-PR ranging between 69 and 94.1 in Baseline and from 72.5 to 97.1 in Incremental model. Of the whole set of methods, Random Forest presented the best performance at all follow-ups [AUC-PR1: 94.1 (2.0); AUC-PR2: 91.2 (1.2); AUC-PR3: 97.1 (1.0); AUC-PR4: 96.5 (1.7)]. Logistic Regression performed well below Random Forest. The top-ranked predictors were common for both models in all follow-ups: birthweight, gestational age, maternal age, and length of hospital stay. Random Forest presented the highest capacity for prediction and provided interpretable predictors. Researchers involved in cohorts can benefit from our robust models to prepare for and prevent loss to follow-up by directing efforts toward individuals at higher risk.