Attrition Rates Research Articles

Comparative efficacy and acceptability of different exercise patterns for reducing cardiovascular events in pre-diabetes: protocol for a systematic review and network meta-analysis of randomised controlled trials

IntroductionExercise has been used to reverse dysglycaemic states in patients with pre-diabetes. Systematic reviews show that exercise is an effective way to reduce the incidence of diabetes, but there is...

Cost-benefit analysis with return on investment of clinical pharmacists in the Military Health System.

The Defense Health Agency comprises more than 700 military medical, dental, and veterinary facilities and provides care to more than 9.6 million beneficiaries. As medication experts, pharmacists identify opportunities to optimize medication therapy, reduce cost, and increase readiness to support the Defense Health Agency's mission. The Tripler Pilot Project and the Army Polypharmacy Program were used to establish a staffing model of 1 clinical pharmacist for every 6,500 enrolled beneficiaries. No large-scale cost-benefit study within the military health care system has been done, which documents the number of clinical interventions and uses established cost-avoidance (CA) data, to determine the cost-benefit and return on investment (ROI) for clinical pharmacists working in the medical treatment facilities. To validate the patient-centered medical home staffing model across the military health care system using the Tripler Pilot Project results to provide a cost-benefit analysis with an ROI. The secondary goal is to describe the interventions, staffing levels, and US Department of Defense-specific requirements impacting the provision of clinical pharmacy. A retrospective analysis of 3 years of encounters by clinical pharmacists in which an intervention was documented in the Tri-Service Workflow (TSWF) form as part of the electronic health record was completed. The analysis used 6 steps to assign CA intervention types and to prevent duplication and overestimation of the ROI. The absolute number of clinical pharmacists was determined using workload criteria defined as at least 20 encounters per month for at least 3 months of each calendar year. The number of clinical pharmacist full-time employees (FTEs) was determined by dividing the number of total active months by 12 months. Attrition was calculated comparing the presence of a unique provider identification between calendar years. The ROI range was calculated by dividing the CA by the total cost of clinical pharmacists using the variables' raw and extrapolated CA based on percentage of documentation template usage and the active clinical pharmacist calculation (absolute and FTE-based). Between January 1, 2017, and December 31, 2019, a total of 1,069,846 encounters by clinical pharmacists were documented in the electronic health record. The TSWF Alternative Input Method form was used by pharmacists to document 616,942 encounters. Forty-three percent of TSWF documented encounters had at least 1 CA intervention. The absolute number of clinical pharmacists associated with a documented encounter in any medical treatment facility ranged from 404 in 2017 to 374 in 2018 and the clinical pharmacist FTEs ranged from 324 in 2017 to 314 in 2019. Annual attrition rates for clinical pharmacists ranged from 15% to 20% (58 to 81 clinical pharmacists) annually. The total CA range was $329,166,543-$534,014,494. The ROI range was between $2 and $4 per dollar spent. This analysis demonstrated that ambulatory care clinical pharmacists in the Military Health System bring value through a positive ROI. Our study also identified a potential shortage of clinical pharmacists within the Air Force and Navy branches impacting medication management. This can have a negative impact on the readiness of service members, one of the leading priorities of the US Department of Defense.

Cognitive remediation for schizophrenia: Clinician perspectives on implementation barriers and facilitators.

Neurocognitive deficits in schizophrenia have a major impact on functioning; however, they remain poorly targeted by available treatment offerings. Cognitive remediation (CR) is effective in improving neurocognition and functioning. Despite clinical guidelines for schizophrenia recommending CR, it is still not readily available in clinical services and sizeable attrition rates are reported in clinical trials. To elucidate the barriers and facilitators of CR access and engagement, we conducted a mixed methods qualitative-dominant study with 12 clinicians in Australia, in 2021, with 1 hr interviews and additional rating scales completed. Thematic analysis highlighted four themes (cognitive symptoms, CR intervention, motivation and engagement in CR, and CR implementation), and 14 subthemes. Clinicians emphasized the broad impact of cognitive deficits and outlined pros and cons of different CR approaches. Several factors were suggested as impacting engagement, including motivation assessments/techniques, neurocognitive insight, illness, and demographic factors. Lack of routine implementation in Australia was unanimously espoused and partly explained by a need for cost-effectiveness analyses, remote and flexible delivery, and increasing service resource provision and staff training in CR. This study offers key insights into CR access, while recommending methods for optimizing CR implementation and dissemination to improve recovery outcomes of people diagnosed with schizophrenia. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Effects of the COVID-19 Pandemic on Nursing Education Programs.

The U.S. Boards of Nursing (BONs) collect annual report data from their nursing programs as part of their approval process. This paper highlights the 2020 and 2021 annual report data on the effect of coronavirus disease 2019 (COVID-19) on all nursing programs in 17 BONs in 2020 and 19 in 2021. Nursing programs answered 16 questions on the effect of COVID-19 on their programs. Because BONs require annual report data, all programs in the participating states answered the questions, which included 798 programs in 2020 and 929 in 2021. Major disruptions in nursing education occurred during the pandemic. Clinical experiences and didactic classes were greatly affected, though alternative strategies were used. Student and faculty attrition rates were particularly high in 2021. The authors call for a national forum where nurse leaders analyze what happened and make recommendations for future crisis events. [J Nurs Educ. 2024;63(5):312-319.].

Addressing attrition in the Indian IT industry: Evaluating the efficacy of a motivation and engagement model

This study traces the development of the Indian IT sector from TCS's founding in 1967 to its emergence as a multibillion-dollar sector in 2023. Many IT firms were set up in India after the establishment of the firm TCS with Tier 1 and Tier 2 categories, based on their size, revenue, and other factors., etc. Analyzed are two of the issues of the IT sector: a high attrition rate and low employee engagement. The reasons behind these are identified, and a methodology is created to raise employee satisfaction and lower the attrition rate in the Indian IT industry. News articles in publications including Times of India, Hindustan Times, Deccan Herald, Economic Times, and LiveMint provided information about the Indian IT industry. Google Scholar provided keywords such as "Indian IT Sector," "Employee Motivation," "Employee Engagement," "Job Satisfaction," and "Attrition." For every keyword searched from 1980 to 2022, the top 20 referenced papers were found. A total of 77 publications were reviewed, and their abstracts were examined to determine their applicability to the study question. Additionally, a number of OB and HRM textbooks were searched for information about the keywords. Definitions of motivation, employee engagement, job satisfaction, and attrition rate that are pertinent to the research study are created from a systematic literature analysis, giving these concepts more conceptual clarity. A model that connects attrition rate, job satisfaction, employee engagement, and motivation is built. Theoretical inferences of the model are examined, including the inclusion of individual characteristics, organisational commitment, and turnover intentions. The model's practical implications, such as its application in the business world and in nations with different cultural norms, are examined. Lastly, a discussion of the study's limitations is included, as the model was created using a limited number of variables.

Improving Shopping Mall Revenue by Real-Time Customized Digital Coupon Issuance

With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer’s churn probability and shopping propensity without the burden of marketing costs in the online shopping mall. Keyword: Customer churn prediction, deep learning, digital marketing, ecommerce, recommendation system.

Can coaching bridge the gap for incoming Latinx graduate students?

ABSTRACT Attrition rates in the first two years of graduate school are high and ∼50% higher for underrepresented students. Here we evaluate an online group coaching intervention using a controlled and an experimental group to determine its impact on skills that are likely predictors of student success, namely the Hallmark of Success indices developed by the National Institutes of Health (NIH). Our intervention includes eight online coaching sessions (four prior to the first year of graduate school and four during the first year of graduate school). Coaching group sessions, led by certified coaches, address Resilience, Risk and Protective Factors, Accountability, Leadership, Teamwork, Professional Networking, and Self-Assessment. We evaluated the change in Hallmark of Success indices with a pre- and post-questionnaire of 44 items used to assess students’ self-evaluation in nine areas. Students in the experimental coaching group showed a significant increase in the Hallmark of Success indices in seven out of nine areas of student performance between the pre- and post-questionnaires compared to the null model of no changes between time periods. In addition, in all of the nine indices of success, we noted significant differences between the pre- and post-surveys for the experimental group. Our experimental design shows that our cost-effective coaching model improves student performance as perceived by the students.

In-depth impact evaluation of binders on the mechanical properties and CO2 capture performance of CaO-based particles

The industrial applications of CaO-based adsorbents for CO2 capture are limited by the challenges of serious sintering in a realistic regeneration condition and elutriation in the fluidized-bed reactors. Although the granulation of CaO-based adsorbents has been extensively investigated, the key indexes such as mechanical strength, adsorption performance, and cyclic stability were investigated independently rather than united for practical applications. Here, we systematically investigated the impacts of binders on the mechanical properties and CO2 capture performance of CaO-based particles granulated using the extrusion method. Among different commercial binders (cement, kaolin, and bentonite), only cement yielded calcined CaO-based particles with the required mechanical strength. At the optimal cement proportion (10%) and particle diameter (3 mm), the “CaO+Cement” particle achieved a compressive strength of ≥20 N and an attrition rate of ≤5%. After granulating, the “CaO+Cement” particle retained a favorable CO2 uptake of 458 mg/g within 10 min, and also exhibited excellent cyclic performance in a realistic regeneration condition (calcination at 920 °C in a CO2 atmosphere), with a decay rate of 0.9% per cycle during long-term cycles. After 50 cycles, the cumulative CO2 uptake for “CaO+Cement” particle was 9.44 g/g, representing a marked increase of 42.9% compared with that of the pure CaO particle. Owing to those multiple advantages of excellent mechanical strength, CO2 adsorption performance, cyclic stability and economic cost, the “CaO+Cement” particle (with a cement proportion of 10% and a diameter of 3 mm) appears to be a promising material for CO2 capture from industrial flue gas at large-scale.

Player Churn Prediction In Free To Play Game Using Ensemble Learning

Player churn is a prevalent challenge in the gaming industry. Most predictions of player churn utilize private datasets that are not easily accessible to the public. This study aims to investigate the performance of Logistic Regression, Random Forest, Support Vector Machines, and Ensemble Learning models using a dataset from a public API for predicting player churn, in comparison to other studies that typically rely on private game logs. In this research, the dataset consists of 418 unique player IDs, with a churn rate of 15%. After training the models, it was found that Logistic Regression and SVM achieved an accuracy of 95%, Random Forest achieved an accuracy of 96%, and Ensemble Learning, with Neural Network as the meta-learner, achieved an accuracy of 92%. These results underscore the validity of using public API data as an alternative data source for predicting player churn. n.

Churn Prediction in Telecommunication Industry: A Comparative Analysis of Boosting Algorithms

The issue of customer churn, which is a major problem in the telecommunications industry, poses several challenges, such as financial implications, client attrition, and increased marketing costs. The advancements achieved in the domain of machine learning and artificial intelligence have significantly expanded the possibilities for forecasting customer churn, presenting a promising resolution for effectively handling customer attrition and enhancing customer retention. This study presents a customer churn prediction model that uses machine learning approaches to assist telecom firms in enhancing customer retention and mitigating churn rates. The study employs machine learning techniques, such as Adaboost, Gradient Boosting, and Extreme Gradient Boosting (XGBoost), in order to evaluate extensive datasets and provide predictions on customer churn via a comparative evaluation. The methodology involves extracting data from the Kaggle data pool, doing further data preparation, and identifying relevant features. The Synthetic Minority Oversampling Technique (SMOTE) is used as a strategy to mitigate the challenges posed by imbalanced data. The dataset is partitioned into training and testing sets at a ratio of 75% to 25%. The XGBoost model demonstrated superior accuracy and recall, positioning itself as the top-performing model among the studied models. The attained accuracy rate was 89.51%. The XGBoost method has a recall rate of 92.48%, which is the highest of the three algorithms evaluated. Gradient boosting follows with a recall rate of 87.69%, while Adaboost achieves a recall rate of 85.13%. These findings underscore the potential of machine learning techniques for addressing the challenges posed by customer churn in the telecommunications industry.

An investigation of the longevity wear assessment during the utilisation of biofuel-diesel blends in a diesel engine: a case study of crude palm and crude jatropha oil

A longevity test was conducted to evaluate the impact of biofuels on engine degradation. Two varieties of blended fuel were generated by adding crude palm oil or crude jatropha oil to B5 diesel. The amounts of additional crude oil were 5, 10, and 15%. The testing period lasted 600 h, which is equivalent to 3–5 years of actual service. Before and after the test, the dimensions of engine elements were measured to determine the level of wear. Inspecting engine components revealed that combining crude bio-oils in proportions ranging from 0 to 15% increased engine wear. Components located in the combustion chamber exhibited greater wear when blended fuels were used, whereas fuel pump and injector components, which were not exposed to the heat, exhibited lower levels of wear. Even though engine wear was observed when mixed fuels were used, the vast majority of wear rates were incredibly low, ranging from 0.001 to 0.002 mm per 100 h. The only exception to this rule was the piston attrition rate, which was measured at 0.007 mm per 100 h. It can be concluded that engine wear is negligible when biofuel is blended with diesel in a ratio not exceeding 15% compared to B5 diesel.

Implementing global positioning system trackers for people with dementia who are at risk of wandering.

The main aim of this study was to evaluate the feasibility and acceptability of using a GPS tracker to mitigate the risks associated with wandering for people with dementia and those caring for them and further evaluate the impact of trackers in delaying 24-hour care and the potential for reducing the involvement of support services, such as the police, in locating patients. We recruited forty-five wearers-carers dyads, and a GPS tracker was issued to each participant. Dyads completed pre-and post-outcome questionnaires after six months, and a use-log of experiences was maintained through monthly monitoring calls. At six months, focus groups were conducted with 14 dyads where they shared ideas and learning. Data analyses were performed on outcome questionnaires, use-log analysis, and focus groups discussion. A 24% (N = 14) attrition rate was recorded, with 76% (N = 34) of the participants completing pre- and post-outcome questionnaires, of which 41% (N = 14) attended four focus group meetings. Participants reported enhanced independence for wearers as fewer restrictions were placed on their movements, peace of mind and reduced burden for the carers with less need to involve police or social services, and delays in 24-hour care. The results supported the feasibility of routine implementation of GPS trackers in dementia care with clear guidance, monitoring and support to family carers on safe use. This could delay admission into 24-hour care as wearers and carers have a greater sense of safety and are better connected should help be required. Studies with larger sample sizes, diverse participants and health economic analysis are needed to develop the evidence base further ahead of the routine implementation of GPS trackers in health and social care services.

Enhanced data augmentation for predicting consumer churn rate with monetization and retention strategies: a pilot study

Customer retention and monetization have since been the pillar of many successful firms and businesses as keeping an old customer is far more economical than gaining a new one – which, in turn, reduce customer churn rate. Previous studies have focused on the use of single heuristics as well as provisioned no retention strategy. To curb this, our study posits the use of the recen-cy-frequency-monetization framework as strategy for customer retention and monetization impacts. With dataset retrieved from Kaggle, and partitioned into train and test dataset/folds to ease model construction and training. Study adopt a tree-based Random Forest ensemble with synthetic minority oversampling technique edited nearest neighbor (SMOTEEN). Various benchmark models were trained to asssess how well each performs against our proposed ensemble. The application was tested using an application programming interface Flask and integrated using streamlit into a device. Our RF-ensemble resulted in a 0.9902 accuracy prior to applying SMOTEENN; while, LR, KNN, Naïve Bayes and SVM yielded an accuracy of 0.9219, 0.9435, 0.9508 and 0.9008 respectively. With SMOTEENN applied, our ensemble had an accuracy of 0.9919; while LR, KNN, Naïve Bayes, and SVM yielded an accuracy of 0.9805, 0.921, 0.9125, and 0.8145 respectively. RF has shown it can be implemented with SMOTEENN to yield enhanced prediction for customer churn prediction using Python

The effectiveness of abstinence-based and harm reduction-based interventions in reducing problematic substance use in adults who are experiencing homelessness in high income countries: A systematic review and meta-analysis: A systematic review.

The effectiveness of abstinence-based and harm reduction-based interventions in reducing problematic substance use in adults who are experiencing homelessness in high income countries: A systematic review and meta-analysis: A systematic review.

Teachers' Emotional Intelligence and Organizational Commitment: A Moderated Mediation Model of Teachers' Psychological Well-Being and Principal Transformational Leadership.

Given the global challenge of increasing teacher attrition and turnover rates, the exploration of factors and mechanisms that improve teachers' organizational commitment has become a pivotal topic in educational research. In this context, the present study examines the influence of teachers' emotional intelligence on their organizational commitment, with a specific inquiry into the mediating role of teachers' psychological well-being and the moderating role of principal transformational leadership, as informed by the broaden-and-build theory of positive emotions and the trait activation theory. We verified this study's hypotheses based on 768 valid questionnaires collected from Chinese primary and secondary school teachers. The results reveal that teachers' emotional intelligence can predict their organizational commitment both directly and indirectly through the mediating role of psychological well-being. Additionally, principal transformational leadership amplifies the positive effect of teachers' emotional intelligence on psychological well-being and, subsequently, organizational commitment. These findings theoretically deepen our understanding of the psychological pathways and the boundary conditions linking teachers' emotional intelligence to their organizational commitment, while also offering valuable practical implications for building a stable and effective teaching workforce.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

Participant recruitment and attrition in surgical randomised trials with placebo controls versus non-operative controls: a meta-epidemiological study and meta-analysis

ObjectiveTo compare differences in recruitment and attrition between placebo control randomised trials of surgery, and trials of the same surgical interventions and conditions that used non-operative (non-placebo) controls.DesignMeta-epidemiological study.Data sourcesRandomised...

Acceptability and feasibility of a short-term group therapy for people with borderline personality disorder symptoms.

This study evaluated the acceptance, feasibility and safety of a short-term group program for adults (18 years and older) and youth (16 to 18 years) with borderline personality disorder (BPD) symptoms. Termed Road Maps, the content and development were informed by common treatment factors identified from evidence-based therapies for BPD. Two-hundred and eight people consented to participate in the research trial and completed baseline measures. Intervention participants rated the acceptability and subjective experience of the group. Attrition rates informed feasibility, and serious adverse events were tracked to identify potential harms. Participant post-group ratings of the group's acceptability and subjective experience were above average across both adult and youth populations. Attrition rate after commencement of group was 38% for adults and 27% among youth. The incidence rate of emergency department presentations was reduced by 41% in the 6 months post-group, relative to 6 months pre-group. The current study provides preliminary support for the acceptability and feasibility of a short-term group therapy program for people with a diagnosis of BPD. Road Maps may be a useful intermediate intervention in a broader model of stepped care. Australian New Zealand Clinical Trials Registry, https://www.anzctr.org.au/ACTRN12622000849796.aspx, (ACTRN12622000849796).

Adaptive design trials in eating disorder research: A scoping review.

This scoping review sought to map the breadth of literature on the use of adaptive design trials in eating disorder research. A systematic literature search was conducted in Medline, Scopus, PsycInfo, Emcare, Econlit, CINAHL and ProQuest Dissertations and Theses. Articles were included if they reported on an intervention targeting any type of eating disorder (including anorexia nervosa, bulimia nervosa, binge-eating disorder, and other specified feeding or eating disorders), and employed the use of an adaptive design trial to evaluate the intervention. Two independent reviewers screened citations for inclusion, and data abstraction was performed by one reviewer and verified by a second. We identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder conducted in the USA and Australia. All employed adaptive treatment arm switching based on early response to treatment and identified a priori stopping rules. None of the studies included value of information analysis to guide adaptive design decisions and none included lived experience perspectives. The limited use of adaptive designs in eating disorder trials represents a missed opportunity to improve enrolment targets, attrition rates, treatment outcomes and trial efficiency. We outline the range of adaptive methodologies, how they could be applied to eating disorder research, and the specific operational and statistical considerations relevant to adaptive design trials. Adaptive design trials are increasingly employed as flexible, efficient alternatives to fixed trial designs, but they are not often used in eating disorder research. This first scoping review identified five adaptive design trials targeting anorexia nervosa, bulimia nervosa and binge-eating disorder that employed treatment arm switching adaptive methodology. We make recommendations on the use of adaptive design trials for future eating disorder trials.

Feasibility and Preliminary Efficacy of American Elderberry Juice for Improving Cognition and Inflammation in Patients with Mild Cognitive Impairment.

Despite data showing that nutritional interventions high in antioxidant/anti-inflammatory properties (anthocyanin-rich foods, such as blueberries/elderberries) may decrease risk of memory loss and cognitive decline, evidence for such effects in mild cognitive impairment (MCI) is limited. This study examined preliminary effects of American elderberry (Sambucus nigra subsp. canadensis) juice on cognition and inflammatory markers in patients with MCI. In a randomized, double-blind, placebo-controlled trial, patients with MCI (n = 24, Mage = 76.33 ± 6.95) received American elderberry (n = 11) or placebo (n = 13) juice (5 mL orally 3 times a day) for 6 months. At baseline, 3 months, and 6 months, patients completed tasks measuring global cognition, verbal memory, language, visuospatial cognitive flexibility/problem solving, and memory. A subsample (n = 12, 7 elderberry/5 placebo) provided blood samples to measure serum inflammatory markers. Multilevel models examined effects of the condition (elderberry/placebo), time (baseline/3 months/6 months), and condition by time interactions on cognition/inflammation outcomes. Attrition rates for elderberry (18%) and placebo (15%) conditions were fairly low. The dosage compliance (elderberry-97%; placebo-97%) and completion of cognitive (elderberry-88%; placebo-87%) and blood-based (elderberry-100%; placebo-100%) assessments was high. Elderberry (not placebo) trended (p = 0.09) towards faster visuospatial problem solving performance from baseline to 6 months. For the elderberry condition, there were significant or significantly trending decreases over time across several markers of low-grade peripheral inflammation, including vasorin, prenylcysteine oxidase 1, and complement Factor D. Only one inflammatory marker showed an increase over time (alpha-2-macroglobin). In contrast, for the placebo, several inflammatory marker levels increased across time (L-lactate dehydrogenase B chain, complement Factor D), with one showing deceased levels over time (L-lactate dehydrogenase A chain). Daily elderberry juice consumption in patients with MCI is feasible and well tolerated and may provide some benefit to visuospatial cognitive flexibility. Preliminary findings suggest elderberry juice may reduce low-grade inflammation compared to a placebo-control. These promising findings support the need for larger, more definitive prospective studies with longer follow-ups to better understand mechanisms of action and the clinical utility of elderberries for potentially mitigating cognitive decline.