Bias-corrected And Accelerated Research Articles

The effectiveness of Hope Groups, a mental health, parenting support, and violence prevention program for families affected by the war in Ukraine: Findings from a pre-post study

BackgroundNearly one in six children lived in war zones in 2023. Evidence-based psychosocial and parenting support has potential to mitigate negative impacts for parents and children co-exposed to war and displacement, especially in relation to mental health and harsh parenting reactions. In the current war in Ukraine, local mental health experts co-created and evaluated, with global experts, the effectiveness of psychosocial and parenting support groups, called ‘Hope Groups’ on improvements in mental health, positive parenting, and violence against children. This paper aimed to assess the effectiveness of psychosocial and parenting support groups, called 'Hope Groups,' on improvements in caregiver mental health, positive parenting, and prevention of violence against children, for families affected by the war in Ukraine, using a pre/post study design. MethodsParticipants (n = 577) included Ukrainian caregivers, 66% (381) of whom were parents and co-residing caregivers of children ages 0–17, while the remaining 34% were non-resident informal caregivers. Internally displaced, externally displaced, and those living at-home in war-torn regions were invited to groups by trained Ukrainian peer facilitators. Using a pre-post design, we compared individual level frequency measures at three time-points – baseline, midline, and endline, to assess changes in 4 mental health, and 9 parenting and child health outcomes. We analyzed these outcomes using paired t-tests to compare outcomes at baseline-to-midline (after 4-sessions) and baseline-to-endline (after 10-sessions), which estimated the mean changes in days per week and associated percent change, during the respective periods; we quantified uncertainties using bias-corrected and accelerated (BCa) bootstrapping with 95% uncertainty ranges for baseline-midline and baseline-endline estimates. We used this same approach for stratified analyses to assess potential effect modification by displacement status and facilitator type. We further used linear models to adjust for age and sex. FindingsCompared to baseline, every mental health, parenting, and child health outcome improved significantly at midline and endline. Mental health ratings showed endline reductions in depressive symptoms of 56.8% (95% CI: -59.0,-54.3; -1.8 days/week), and increases in hopefulness, coping with grief, and self-care, ranging from 62.0% (95% CI: 53.6,71.3; 2.2 days/week) to 77.0% (95% CI: 66.3,88.3; 2.2 days/week). Significant improvements in parenting and child health outcomes included monitoring children, reinforcing positive behavior, supporting child development, protecting child, nonviolent discipline, and child verbalizing emotions. By endline, emotional violence, physical violence, and child despondency had dropped by 57.7% (95% CI: -63.0%,-51.9; -1.3 days/week), 64.0% (95% CI: -79.0,-39.5; -0.22 days/week), and 51.9% (95% CI: -45.1,-57.9; -1.2 days/week), respectively. Outcomes stratified by displacement status remained significant across all groups, as did those according to facilitator type (lay versus professional). InterpretationThis study demonstrates preliminary evidence, using a brief survey and pre-post design as is appropriate for acute and early protracted emergency settings, of the feasibility and effectiveness of Hope Groups for war-affected Ukrainian caregivers, on improved mental health, positive parenting, and reduced violence against children.

Model Independent and Dependent Methods for Dissolution Profiles Comparison: Can we take the Road Less Travelled?

Dissolution testing plays an important role in pharmaceutical development due to its ability to provide critical insight into in vivo performance. Dissolution testing during routine pharmaceutical development can ensure batch-to-batch consistency and helps to ensure that the commercial batches are of representative quality to that of studied in the pivotal clinical studies. For comparing the dissolution profiles, there are multitude of approaches suggested by various regulatory agencies that consist of model independent and dependent approaches. The present review aims to summarize the current understanding of dissolution profile similarity approaches. Dissolution profile comparison approaches suggested by various regulatory agencies were compared and contrasted. Further, detailed procedural aspects were provided for the determination of similarity using model independent and dependent approaches. Model independent approaches such as f2 bootstrap, and Multivariate Statistical Distance (MSD) were portrayed from a practical perspective. Advanced tools such as Bias-Corrected and accelerated (BCa) for f2 bootstrap were discussed in depth. Further, model dependent approaches such as zero order, and Weibull models were discussed from realistic scenarios. Finally, the utility of modeling and simulation approaches such as physiologically based pharmacokinetics model (PBPK) and physiologically based biopharmaceutics model (PBBM) were discussed in the context of their ability and potential to supersede traditional dissolution dissimilarity. Overall, this article acts as a ready-to-use guide for pharmaceutics and biopharmaceutics scientists for effective methodologies for dissolution profile comparison for internal decision-making and regulatory justifications.

Plasma neurofilament light chain is not elevated in people with first-episode psychosis or those at ultra-high risk for psychosis

IntroductionNeurofilament light chain (NfL), a blood biomarker of neuronal injury, shows promise in distinguishing neurodegenerative disorders from psychiatric conditions. This is especially relevant in psychosis, given neurological conditions such as autoimmune encephalitis and Niemann Pick Type C disease (NPC) may initially present with psychotic symptoms. Whilst NfL levels have been studied in established schizophrenia cases, their levels in first-episode psychosis (FEP) and ultra-high risk (UHR) for psychosis individuals remain largely unexplored. This study aimed to compare plasma NfL in people with FEP or UHR with healthy controls, as well as explore its associations with clinical data. MethodWe retrospectively analysed plasma NfL in 63 participants, consisting of 29 individuals with FEP, 10 individuals with UHR, and 24 healthy controls. We used general linear models (GLM), which were bootstrapped, to compute bias-corrected and accelerated (BCa) 95 % confidence intervals (CIs). ResultsMean NfL levels were 5.2 pg/mL in FEP, 4.9 pg/mL in UHR, and 5.9 pg/mL in healthy controls. Compared to healthy controls, there were no significant differences in NfL levels in the FEP group (β = −0.22, 95 % CI [−0.86, 0.39], p = 0.516) nor UHR group (β = −0.37, 95 % CI [−0.90, 0.19], p = 0.182). There were no significant associations between NfL levels and clinical variables in the FEP group. DiscussionOur study is the first to demonstrate that plasma NfL levels are not significantly elevated in individuals at UHR for psychosis compared to healthy controls, a finding also observed in the FEP cohort. These findings bolster the potential diagnostic utility of NfL in differentiating between psychiatric and neurodegenerative disorders.

Unveiling the relationship of physical literacy with muscular fitness and muscle-strengthening activities in adolescents: the EHDLA study

ObjectiveThis study evaluated the relationship between physical literacy (PL) and muscular fitness (MF) as well as muscle-strengthening activities (MSA) in adolescents.MethodsA secondary cross-sectional study included 823 adolescents (45.1% boys) from...

Exploring the link between perceived physical literacy and academic performance outcomes: insights from the EHDLA study

ObjectiveThe aim of this study was to verify the relationship between perceived physical literacy (PPL) and academic performance outcomes among Spanish adolescents aged 12–17 years.MethodsThis cross-sectional study is a secondary examination utilizing data derived from the Eating Healthy and Daily Life Activities (EHDLA) project. The Spanish Perceived Physical Literacy Instrument for Adolescents (S-PPLI) was used to evaluate the PPL of the participants. To determine academic performance, the numerical grades for each subject were added together and then divided by the total number of subjects.ResultsOverall, a positive trend in the association between S-PPLI scores and academic performance was observed. We identified two significant ranges within the S-PPLI scores. First, there was a significant range between 9 and 33 points on the S-PPLI, indicating an inverse association with academic performance. Second, another significant range was observed at scores of 34 points or higher on the S-PPLI, suggesting a positive association with academic performance. Participants with low PPL had the lowest academic performance (mean = 6.4; bias-corrected and accelerated (BCa) bootstrapped 95% confidence interval (CI): 6.2–6.6). Conversely, those adolescents with high PPL had the highest academic performance (mean = 6.9; BCa bootstrapped 95% CI: 6.6–7.1). Significant differences were found between low PPL and medium PPL (p-adjusted = 0.031) and between low PPL and high PPL (p-adjusted = 0.031).ConclusionsIncreased physical literacy could be a relevant factor for achieving greater academic performance in adolescents. This study has potential implications for physical education instructors, school leaders, and healthcare practitioners.

Dissolution Profiles Comparison Using Conventional and Bias Corrected and Accelerated f2 Bootstrap Approaches with Different Software's: Impact of Variability, Sample Size and Number of Bootstraps.

Dissolution profiles comparison is an important element in order to support biowaivers, scale-up and post approval changes and site transfers. Highly variable dissolution can possess significant challenges for comparison and f2 bootstrap approach can be utilized in such cases. However, availability of different types of f2 and confidence intervals (CI) methods indicates necessity to understand each type of calculation thoroughly. Among all approaches, bias corrected and accelerated (BCa) can be an attractive choice as it corrects the bias and skewness of the distribution. In this manuscript, we have performed comparison of highly variable dissolution data using various software's by adopting percentile and BCa CI approaches. Diverse data with different variability's, number of samples and bootstraps were evaluated with JMP, DDSolver, R-software, SAS and PhEq. While all software's yielded similar observed f2 values, differences in lower percentile CI was observed. BCa with R-software and JMP provided superior lower percentile as compared to other computations. Expected f2 recommended by EMA has resulted as stringent criteria as compared to estimated f2. No impact of number of bootstraps on similarity analysis was observed whereas number of samples increased chance of acceptance. Variability has impacted similarity outcome with estimated f2 but chance of acceptance enhanced with BCa approach. Further, freely available R-software can be of attractive choice due to computation of various types of f2, percentile and BCa intervals. Overall, this work can enable regulatory submissions to enhance probability of similarity through appropriate selection of number of samples, technique based on variability of dissolution data.

Monitoring reproduction in fish: Assessing the adequacy of ogives and the predicted uncertainty of their L50 estimates for more reliable biological inferences

In fisheries management, the L50 is a commonly-monitored reproductive trait reflecting the sex-specific length (L) at which 50% of the fish are expected to exhibit developing gonads near the spawning period. Although different methods exist to estimate the L50, it is often obtained from a binomial generalized linear model (GLM) fitted with a logit link. To provide reliable parameter estimates for the production of the ogive from which the L50 is obtained, the logit link must be correctly specified, a prerequisite that is not always verified nor true. To identify the most-likely ogive, adequacy tests allowing to detect a lack-of-fit or any link misspecification should ideally be applied to competing binomial models that also consider alternative links, such as the probit and complementary log-log (cloglog). Because multiple estimates are often compared, their uncertainties should also be adequately quantified to detect any potential change, but no consensus exists about the method offering the best coverage probability. Here, we used a large-scale dataset of 23,681 walleye (Sander vitreus) females sampled in 90 harvested populations over two decades in Québec, Canada, to first estimate the L50 uncertainty under different simulation scenarios to identify the most accurate method regarding nominal coverage. Then, of 46 populations sampled in two different surveys, we assessed which link provided the best adequate estimation for each survey-specific L50 and used associated 84% CIs to determine if a change occurred based on whether they overlapped. The influence of gonad data characteristics on L50 uncertainty was also examined before assessing between-survey L50 variation with competing GLMs as an alternative approach. The analysis of the simulated data indicated that the Monte Carlo approach with bias-corrected and accelerated (BCa) interval offered the best coverage probability. The empirical analyses revealed that the probit was the most often adequate link to estimate the L50, followed by the cloglog, whereas the logit ranked last. When controlling for sample size, the level of L50 uncertainty increased with the percentage of overlap in length between walleye females with (1) and without (0) developing gonads, whereas it progressively decreased as the percentage of 1’s in the sample increased to then inversed, indicating that more balanced binary data yielded less L50 uncertainty. All analyses revealed that for a “true” or estimated effect size of ≤ 10%, the survey-specific 84% CIs often overlapped despite statistical support for a difference found in the best-supported GLM comparing both surveys. Overall, our study indicates that many analytical considerations, from gonad data structure characteristics, model adequacy assessment, up to how the L50 uncertainty is quantified and compared, need to be accounted for to achieve more reliable statistical inferences regarding the observed variation in this reproductive trait.

Second Victims among Austrian Pediatricians (SeViD-A1 Study).

(1) Background: The second victim phenomenon (SVP) plays a critical role in workplace and patient safety. So far, there are limited epidemiological data on the SVP in German-speaking countries. Some studies have been carried out in Germany, but so far, no quantitative studies have been carried out in Austria examining the prevalence, symptom load and preferred support measures for second victims (SVs). This study therefore examines the SVP among Austrian pediatricians. (2) Methods: A nationwide, cross-sectional and anonymous online study was conducted using the SeViD questionnaire (Second Victims in Deutschland) including the Big Five Inventory-10 (BFI-10). Statistical analysis included binary-logistic and multiple linear regression with the bootstrapping, bias-corrected and accelerated (BCa) method based on 1000 bootstrap samples. (3) Results: Of 414 Austrian pediatricians, 89% self-identified as SVs. The main cause of becoming an SV was the unexpected death or suicide of a patient. High neuroticism and extraversion values as well as working in outpatient care positively correlated with having experienced the SVP. A preferred support strategy was access to legal counseling. (4) Conclusions: Austrian pediatricians have the highest SVP prevalence measured with the SeViD questionnaire. Further research should focus on prevention strategies and intervention programs.

A REVIEW OF MANAGING CARBON EMISSIONS FOR SUSTAINABLE SUPPLY CHAIN MANAGEMENT

Carbon emissions were one of the most critical contributors to climate change, and the recent climate agreement had agreed for all participating countries to reduce their emissions. Malaysia has pledged to reduce 45 per cent of its carbon emissions by 2025. However, reducing carbon emissions from the industry was still limited due to voluntary reporting, particularly among manufacturers. Thus, it is ambitious for the Malaysian government to achieve its target by 2025. A complete review is performed to go through the literature and determine the review's research path. This review aims to investigate the low carbon performance based on the manufacturing industry's low carbon supply chain practices. The findings show that low carbon production was insignificant in reducing overall carbon emissions. In contrast, the rest of the low carbon supply chain practices were by the Natural Resource-Based-View (NRBV) theory. It helps to identify companies' low carbon supply chain practices to reduce carbon emissions and meet environmental regulations. Additionally, the review also provided new insight into mediating effect that contributes to the organizational theory of NRBV. The theoretical hypotheses were tested with bias-corrected and accelerated (BCA) bootstrap confidence intervals. The instrument used in this review is a questionnaire, and the questionnaire consists of five (5) scales which tapped on (LCSCM), energy management, and low carbon performance. Responses from the respondent were collected via an online survey between Malaysian manufacturing industries. SmartPLS was used to perform the model and structural analyses. The result shows that LCSCM influence carbon performance directly and indirectly, so the hypotheses are supported. This review is one of the earliest attempts to record empirical evidence regarding companies’ low carbon supply chain practices and performance. Thus, the review shows results can be used and extended to develop low-carbon supply chain frameworks.

Impact of Social Isolation, Physician-Patient Communication, and Self-perception on the Mental Health of Patients With Cancer and Cancer Survivors: National Survey Analysis.

Cancer is perceived as a life-threatening, fear-inducing, and stigmatized disease. Most patients with cancer and cancer survivors commonly experience social isolation, negative self-perception, and psychological distress. The heavy toll that cancer takes on patients continues even after treatment. It is common for many patients with cancer to feel uncertain about their future. Some undergo anxiety, loneliness, and fear of getting cancer again. This study examined the impact of social isolation, self-perception, and physician-patient communication on the mental health of patients with cancer and cancer survivors. The study also explored the impact of social isolation and physician-patient communication on self-perception. This retrospective study used restricted data from the 2021 Health Information National Trends Survey (HINTS), which collected data from January 11, 2021, to August 20, 2021. We used the partial least squares structural equation modeling (PLS-SEM) method for data analysis. We checked for quadratic effects among all the paths connecting social isolation, poor physician-patient communication, mental health (measured using the 4-item Patient Health Questionnaire [PHQ-4]), and negative self-perception. The model was controlled for confounding factors such as respondents' annual income, education level, and age. Bias-corrected and accelerated (BCA) bootstrap methods were used to estimate nonparametric CIs. Statistical significance was tested at 95% CI (2-tailed). We also conducted a multigroup analysis in which we created 2 groups. Group A consisted of newly diagnosed patients with cancer who were undergoing cancer treatment during the survey or had received cancer treatment within the last 12 months (receipt of cancer treatment during the COVID-19 pandemic). Group B consisted of respondents who had received cancer treatment between 5 and 10 years previously (receipt of cancer treatment before the COVID-19 pandemic). The analysis indicated that social isolation had a quadratic effect on mental health, with higher levels of social isolation associated with worse mental health outcomes up to a certain point. Self-perception positively affected mental health, with higher self-perception associated with better mental health outcomes. In addition, physician-patient communication significantly indirectly affected mental health via self-perception. The findings of this study provide important insights into the factors that affect the mental health of patients with cancer. Our results suggest that social isolation, negative self-perception, and communication with care providers are significantly related to mental health in patients with cancer.

Bootstrap Correlation Confidence Interval Estimation: The Positive Impact of a Symmetric Distribution

Estimating confidence intervals (CIs) for the correlation has been a challenge because the correlation sampling distribution changes depending on the correlation magnitude. The Fisher z-transformation was one of the first attempts at estimating correlation CIs but has historically shown to not have acceptable coverage probability if data were non-normal. Bootstrap methods for correlation CIs were then proposed but have been met with mixed results. A recent study consolidating some of the previous literature found that the bootstrap correlation CIs had acceptable coverage probability for non-normal data if sample size was large enough. Additionally, it was found that pairing a normal distribution with any other distribution resulted in better coverage probability for all correlation CIs investigated. Notably, the Fisher z-transformation CI had consistent acceptable coverage probability in this case. Therefore, the purpose of the current study was to investigate if this normality effect extends to other distributions that are symmetric. It was found that the Fisher z-transformation, percentile bootstrap (PB), and bias-corrected and accelerated (BCa) CIs all benefited from having data that came from a symmetric distribution. However, the PB and BCa CIs did not have acceptable coverage probability when a uniform distribution was paired with a chi-square (df = 1).

Multidimensional scale development and validation: university service quality (UNIQUAL)

PurposeThis study aims to develop an assessment scale for university service quality (university SQ) and examine University Service Quality assessment model (UNIQUAL) of higher education during the pandemic.Design/methodology/approachTwo studies applied a mixed-method design to develop and validate the UNIQUAL scale. In-depth interviews and literature reviews were conducted to refine the initial dimensions and items of UNIQUAL in Study 1. Item analysis, EFA and CFA were then conducted to purify item refinement, scale refinement, purification and validation in Study 2. Finally, a confirmed UNIQUAL model was analyzed via partial least squares structural equation modeling (PLS-SEM) using Smart-PLS 4.0.FindingsThe research confirms the four-factor structure of UNIQUAL, with a total of 16 items, to be a valid and reliable scale for the assessment of the service quality (SQ) of universities. Having adopted the bias-corrected and accelerated (BCa) bootstrap approach to study 5,000 subsamples from 27 countries, the authors found “responsiveness” and “empathy” to be significantly associated and have positive relationships with students' satisfaction with university SQ. Furthermore, university SQ and satisfaction were mediated by “health and safety” concerns.Practical implicationsThe newly developed UNIQUAL scale would be of value to educators and authorities of higher education to assess the SQ of their universities to enhance the effectiveness of student learning. The improvement in satisfaction with higher education's SQ ultimately helps in retaining both international and local students amidst concerns about traveling and studying during the pandemic.Social implicationsCOVID-19 has affected the private and public sectors worldwide. Millions of students have been affected by schools being shut down and substituted with distance-learning programs. Thus, the assessment of the quality of university services has become an important support mechanism for retaining the sustainability of higher education.Originality/valueThe UNIQUAL scale provides a conceptual model and validates an assessment tool. The research hypotheses confirm the relationship between university SQ and satisfaction from the perspective of international students.

Subjective Cognitive Decline Is Associated with Health-Related Quality of Life in the Middle-Aged to Elderly Population.

Subjective cognitive decline (SCD) is considered to be a preliminary stage of dementia, and its prevalence is increasing with age. We aimed to study the association of SCD with health-related quality of life (HRQoL) in a large population-based sample. We analyzed data of the first 10,000 participants from the Hamburg City Health Study in Germany, a single center prospective cohort study, aged between 45 and 74 years that scored higher than 25 points in the Mini-Mental State Examination and had no known pre-existing dementia. HRQoL was assessed by the EQ-5D-5 L index, as well as the mental (MCS) and physical component summary (PCS) score of the Short Form-8. We computed linear regression analyses with 99% bias-corrected and accelerated (BCa) confidence intervals (CI) from 10,000 bootstrap samples to investigate the association between SCD and different indicators of HRQoL, while controlling for depression (PHQ-9), age, sex, and education as potential confounders. Of 7,799 eligible participants (mean (SD) age 62.01 (8.41) years, 51.1% female), 3,708 (47.5%) reported SCD. Participants with SCD were older (62.7 versus 61.4 years) and more frequently female (54.2% versus 48.2%). SCD was independently associated with a lower EQ-5D-5 L index (β=-0.01, 99% BCa CI = [-0.020, -0.003], p < 0.001) and PCS (β=-1.00, 99% BCa CI = [-1.48, -0.51], p < 0.001) but not with MCS score. In a population of middle-aged to elderly participants, there is a significant negative association between SCD and HRQoL across different instruments of HRQoL measurement independent of depression, demographics, and education.

The Automated Bias-Corrected and Accelerated Bootstrap Confidence Intervals for Risk Measures

Different approaches to determining two-sided interval estimators for risk measures such as Value-at-Risk (VaR) and conditional tail expectation (CTE) when modeling loss data exist in the actuarial literature. Two contrasting methods can be distinguished: a nonparametric one not relying on distributional assumptions or a fully parametric one relying on standard asymptotic theory to apply. We complement these approaches and take advantage of currently available computer power to propose the bias-corrected and accelerated (BCA) confidence intervals for VaR and CTE. The BCA confidence intervals allow the use of a parametric model but do not require standard asymptotic theory to apply. We outline the details to determine interval estimators for these three different approaches using general computational tools as well as with analytical formulas when assuming the truncated Lognormal distribution as a parametric model for insurance loss data. An extensive simulation study is performed to assess the performance of the proposed BCA method in comparison to the two alternative methods. A real dataset of left-truncated insurance losses is employed to illustrate the implementation of the BCA-VaR and BCA-CTE interval estimators in practice when using the truncated Lognormal distribution for modeling the loss data.

Training value, employee internal states and training transfer: examining the web of relationships

Purpose This study aims to examine the web of relationships among training value, employee internal states (psychological empowerment, employee engagement and motivation to transfer) and training transfer. Design/methodology/approach Data is obtained from different categories of employees a few months after attending different training programs organized by their organization. Structural equation modeling is used to analyze the data. Bias-corrected and accelerated (BCa) confidence intervals are used to determine the significance of the hypothesized paths. Findings This study finds that training value relates to motivation to transfer, psychological empowerment and employee engagement. Motivation to transfer also relates to training transfer and employee engagement. Again, psychological empowerment relates to motivation to transfer, employee engagement and training transfer. Concerning the mediated relationship, psychological empowerment and motivation to transfer fully mediate the relationship between training value and training transfer. Practical implications Internal states must be the focus of human resource department (HRD) scholars in their quest to discover training transfer improvement mechanisms. HRD practitioners and organizations generally should also prioritize the enhancement of the internal states of employees to aid training transfer. Originality/value In this study, training transfer facilitating factors particularly employee internal states are explored by examining the web of relationships comprising training value, motivation to transfer, psychological empowerment, employee engagement and training transfer in a pentagonal model using a homogeneous sample with a common understanding of training transfer due to the similarities in their training as well as job conditions.

Associations among boredom proneness, attachment styles, and compulsive sexual behavior

ABSTRACT Introduction It has been hypothesized that compulsive sexual behavior (CSB) may be a result of a biological vulnerability for affective dysregulation and impulsive control, which may emerge as a result of underlying neurobiological disruptions, childhood adversity, or attachment instability. Boredom proneness and attachment style have been shown to be empirical components associated with both CSB and dysphoric mood states. Thus, individuals who are prone to boredom may use CSB to mitigate negative affect, whereas individuals who have an attachment instability may use CSB to regulate negative affect linked with their insecure attachment. Investigations of these components in association with CSB may benefit in understanding the function of emotional dysregulation in individuals who experience nonparaphilic out-of-control or dysregulated sexual behavior patterns. Objective This study explores associations among boredom proneness, attachment styles, and CSB in order to understand the underlying mechanisms of emotion dysregulation. Method Participants were recruited through MTurk to obtain a sample of adults (age 18+) that is representative of the US-population in terms of gender and race-ethnicity. Participants (n=879) completed scales, including the Compulsive Sexual Behavior Inventory (CSBI-13); the Attachment Style Questionnaire (ASQ-22) measuring by subscales Fearful, Preoccupied, Secure, and Dismissing attachment styles; and the Short Boredom Proneness Scale (SBPS-8). The scores are interpreted as follows, the higher the score, the greater the problem or the characteristic of the assessed condition. Pearson product-moment correlations were used to estimate the associations between variables, and multiple mediation analyses were conducted using PROCESS_v3.5.4: outcome variable=CSBI-13, predictor=SBPS-8, and mediator variables=four subscales of ASQ-22. Effect significance was estimated using the test bias-corrected and accelerated (BCa) confidence interval of 95% and bootstrapping technique (n=5000). Results The CSBI-13 had a significant medium positive correlation with the SBPS-8 (r=.48) and the Fearful (r=.34) and Preoccupied (r=.37) subscales on the ASQ-22. A small significant and negative correlation was found for the Secure attachment style (r=-.20). No significant correlation was identified between the CSBI-13 and the Dismissing subscale. The mediation analyses indicated that the Fearful and Preoccupied subscales of ASQ-22 were significant mediators in the association between boredom and CSB, explaining 15% and 14% of the association, respectively. Conclusion The current findings demonstrate that boredom proneness and insecure attachment styles, namely those at the high-end of the anxiety dimension (i.e.,Fearful and Preoccupied), are positively associated with CSBI-13 scores. These findings support the theorized associations between emotion dysregulation and CSB. When boredom proneness and insecure attachment styles were combined, mediation results indicated that a small percentage of the explanation was transferred from one to the other. This indicates that they are distinct psychological components, despite some overlap and common internal mechanisms relating to dysphoric states, most likely related to a shared anxiety component. Furthermore, reiterated that emotional dysregulation is a complex process in CSB, making it difficult to distinguish whether a negative state and/or an unbalanced aspect of one's existence are a cause and/or an effect. While additional research is necessary, these findings suggest that clinicians should evaluate emotional dysregulation components in a multifactor and comprehensive manner when diagnosing CSB. Disclosure Work supported by industry: no.

Statistical Inference for a Simple Step Stress Model with Competing Risks Based on Generalized Type-I Hybrid Censoring

Abstract This paper investigates a simple step-stress accelerated lifetime test (SSALT) model for the inferential analysis of exponential competing risks data. A generalized type-I hybrid censoring scheme is employed to improve the efficiency and controllability of the test. Firstly, the MLEs for parameters are established based on the cumulative exposure model (CEM). Then the conditional moment generating function (MGF) for unknown parameters is set up using conditional expectation and multiple integral techniques. Thirdly, confidence intervals (CIs) are constructed by the exact MGF-based method, the approximate normality-based method, and the bias-corrected and accelerated (BCa) percentile bootstrap method. Finally, we present simulation studies and an illustrative example to compare the performances of different methods.

Reliability analysis for q‐Weibull distribution with multiply Type‐I censored data

AbstractThe widely used Weibull distribution could be generalized to be q‐Weibull distribution. To fill out the gap in existing literature, the reliability is studied for q‐Weibull distribution with multiply Type‐I censored data, which is the general form of Type‐I censored data. The point estimates and confidence intervals (CIs) for q‐Weibull parameters and reliability parameters such as the reliability and remaining lifetime are all focused on. The maximum likelihood estimates (MLE) are obtained by maximizing the likelihood function and transforming it to an unconstrained optimization problem. The least‐square estimates (LSEs) are proposed by minimizing the single‐variable profile error function derived from reducing the previous multivariable error function. These improvements could make the computation of point estimates efficient. Concerning the CIs, the asymptotic normality of log‐transformed MLE is used to guarantee they fall into the value ranges. Particularly, the closed form for the Fisher information matrix is derived using the missing information principal and is combined with the delta method to construct the CIs for reliability. Besides, the bias‐corrected and accelerated (BCa) bootstrap method is also applied. Further, a Monte Carlo simulation study is conducted to compare different point estimates and CIs. Finally, an illustrative example is presented to show the application of the study in this paper.

Fostering low-carbon production and logistics systems: framework and empirical evidence

This work proposes and empirically tests a new framework for evaluating the relationship between stakeholder pressures, the adoption of low-carbon operations practices and firms’ carbon performance. It seeks to expand upon stakeholder theory and the natural-resource-based view (NRBV) to understand further the role of operations management in a low-carbon environment. Our theoretical hypotheses were tested through the Partial Least Squares method with bias-corrected and accelerated (BCA) bootstrap confidence intervals. The key findings encapsulate a mixture of expected and unexpected research results: (i) stakeholder pressures influence both barriers and motivators for decarbonising operations management practices; (ii) a variety of barriers and motivators significantly affect the adoption of low-carbon operations management practices; (iii) developing positive relationships with stakeholders is important to overcome barriers from the external environment and enhance organisational competitiveness; (iv) low-carbon operations management has an overall effect on firms’ carbon performance; However, unexpectedly: (v) firms seem to face difficulties in understanding stakeholder pressures when developing low-carbon products and logistics, due to a lack of awareness of the sources of barriers to the adoption of low-carbon management practices; (vi) in terms of stakeholders, competitors tend to exert significant pressure towards the adoption of low-carbon operations, while government does not; (vii) more research is necessary to better understand the apparent weak link between low-carbon logistics and firms’ low-carbon performance.

Interval Parameter Estimation of Quantile Regression Using Bca-Bootstrap Approach with Application to Open Unemployment Rate Study

Parameter estimation in the linear regression model using ordinary least square (OLS) method is less precise to analyze data containing outliers. It is because outliers can cause unstable parameter estimate. In addition, the existence of outliers causes residuals to be larger so that the residual’s variance is not constant (heteroscedasticity). One model that is able to overcome the effect of outliers is quantile regression because it can accommodate the non-homogeneous variances in modeling. In this study, the confidence interval of the parameter estimate in the quantile regression model was obtained, i.e., the Bias-Corrected and accelerated (BCa) bootstrap method. The proposed method was applied in modeling the open unemployment rate in Indonesia in 2017. The quantile value used in this study is quantile 0.05, 0.5, and 0.95 with 1500 resampling in BCa-bootstrap approach. The empirical result shows that the best quantile regression model is obtained at the value of quantile 0.95 which has a Pseudo R2 value is 60.45 percent. The model at quantile 0.95 shows that the percentage of youth, economic growth rate, and labor force participation rate have a significant effect on the open unemployment rate in Indonesia.