Explanatory Model Research Articles

How Environmental Policy Perception and Social Media Use Impact Pro-Environmental Behavior: A Moderated Mediation Model Based on the Theory of Planned Behavior

Pro-environmental behavior (PEB) is an essential source for solving environmental problems and implementing sustainable development. This study reveals the antecedent mechanisms of PEB from the joint perspective of environmental policy perception (EPP) and social media use (SMU). We developed a moderated mediation model based on the theory of planned behavior (TPB) and tested the hypotheses using questionnaire data from 408 residents in Tianjin, China. The results indicated that EPP positively affects PEB and that attitude and perceived behavioral control in the TPB framework mediate the relationship between EPP and PEB. SMU not only has a positive effect on PEB but also remotely mediates the effect of EPP on PEB together with perceived behavioral control. Furthermore, gender and age have a positive moderating impact on EPP. This study provides the first theoretical and empirical explanation of how EPP influences PEB within a systematic TPB framework while also introducing SMU as an innovative expansion of the explanatory model. This study also provides recommendations for policymakers on environmental management practices.

Pengaruh Store Atmosphere dan Kualitas Pelayanan terhadap Kepuasan Konsumen di Leng Coffee, Pekanbaru

A comfortable and safe store atmosphere, coupled with excellent, high-quality service, are important factors that contribute to consumer satisfaction. Leng Coffee Pekanbaru, has strategically developed a safe and pleasant store atmosphere, and has implemented the best service which of course not only meets but also exceeds consumer expectations, as a strategy to face intense business competition. This research aims to assess the influence of store atmosphere and service quality on consumer satisfaction at Leng Coffee Pekanbaru. This research uses a descriptive statistical approach and quantitative statistics with an explanatory research model. The sample consisted of 100 respondents selected through the incidental sampling method. The main research instrument used was a questionnaire. The results of this research found that store atmosphere and service quality have a positive influence on consumer satisfaction both partially and simultaneously at Leng Coffee Pekanbaru.

Sense of control and noise sensitivity affect frustration from interfering noise.

In order to develop effective strategies to address noise annoyance, it is essential to develop an explanatory model of the psychological mechanism(s) by which noise becomes annoying. Two online studies were conducted in which a total of 193 participants completed speech perception tasks with varying degrees of background noise. Signal-to-noise ratio levels ranged from -2 to -10 dB. The crucial manipulation in both experiments concerned participants' sense of control over the noise level in the task. Dependent measures were task performance, a self-reported measure of frustration with the task, and self-reported sensitivity (trait) to noise. Results showed small but significant effects of noise sensitivity and sense of control on subjective frustration. In both experiments, more noise-sensitive individuals expressed greater frustration than did those reporting less noise sensitivity. In addition, in experiment 2 there was a significant interaction between sense of control and noise level. Listeners under the higher noise-level conditions expressed relatively similar degrees of frustration irrespective of their sense of control, while those under the lower noise-level condition showed lower frustration with greater sense of control. Results support Stallen's [(1999). Noise Health 1(3), 69-79] theoretical framework of noise annoyance, but also suggest the need for further research under more ecologically plausible conditions.

Examining Texting in the Classroom Between Adolescents Without and With Learning Disabilities: Do Classroom Climate and Academic Boredom Matter?

The present study investigated texting in the classroom through written (SMS) and instant (social media) messages comparatively between adolescents without and with LD. Also, applying the Social Cognitive Theory, the role of perceived classroom climate and academic boredom in texting in the classroom was co-examined through an explanatory mediation model. Overall, 439 students (59% girls) without LD and 408 students (47% girls) with LD, who both attended randomly selected Greek junior high schools of Mainstream Education and used a mobile phone, completed self-reported scales about the variables involved with reference to the Modern Greek language course. The results showed that texting in the classroom (through written/instant messages) was above average for both student subgroups, regardless of their gender. However, within a comparative perspective, texting in the classroom through instant (social media) messages was higher for students with LD. Furthermore, in the relationship between perceived classroom climate and texting in the classroom (through written/instant messages) academic boredom proved a full mediator for students without LD and a partial mediator for students with LD. The findings imply the need for psychoeducational interventions in the context of specific school courses, to strengthen differentiated dimensions of psychosocial classroom climate for adolescents without and with LD. Thus, positive academic emotions and minimized disruptive texting in the classroom could be achieved.

Machine learning prediction and explanatory models of serious infections in patients with rheumatoid arthritis treated with tofacitinib

BackgroundPatients with rheumatoid arthritis (RA) have an increased risk of developing serious infections (SIs) vs. individuals without RA; efforts to predict SIs in this patient group are ongoing. We assessed the ability of different machine learning modeling approaches to predict SIs using baseline data from the tofacitinib RA clinical trials program.MethodsThis analysis included data from 19 clinical trials (phase 2, n = 10; phase 3, n = 6; phase 3b/4, n = 3). Patients with RA receiving tofacitinib 5 or 10 mg twice daily (BID) were included in the analysis; patients receiving tofacitinib 11 mg once daily were considered as tofacitinib 5 mg BID. All available patient-level baseline variables were extracted. Statistical and machine learning methods (logistic regression, support vector machines with linear kernel, random forest, extreme gradient boosting trees, and boosted trees) were implemented to assess the association of baseline variables with SI (logistic regression only), and to predict SI using selected baseline variables using 5-fold cross-validation. Missing values were handled individually per prediction model.ResultsA total of 8404 patients with RA treated with tofacitinib were eligible for inclusion (15,310 patient-years of total follow-up) of which 473 patients reported SIs. Amongst other baseline factors, age, previous infection, and corticosteroid use were significantly associated with SI. When applying prediction modeling for SI across data from all studies, the area under the receiver operating characteristic (AUROC) curve ranged from 0.656 to 0.739. AUROC values ranged from 0.599 to 0.730 in data from phase 3 and 3b/4 studies, and from 0.563 to 0.643 in data from ORAL Surveillance only.ConclusionsBaseline factors associated with SIs in the tofacitinib RA clinical trial program were similar to established SI risk factors associated with advanced treatments for RA. Furthermore, while model performance in predicting SI was similar to other published models, this did not meet the threshold for accurate prediction (AUROC > 0.85). Thus, predicting the occurrence of SIs at baseline remains challenging and may be complicated by the changing disease course of RA over time. Inclusion of other patient-associated and healthcare delivery-related factors and harmonization of the duration of studies included in the models may be required to improve prediction.Trial registrationClinicalTrials.gov: NCT00147498; NCT00413660; NCT00550446; NCT00603512; NCT00687193; NCT01164579; NCT00976599; NCT01059864; NCT01359150; NCT02147587; NCT00960440; NCT00847613; NCT00814307; NCT00856544; NCT00853385; NCT01039688; NCT02187055; NCT02831855; NCT02092467.

Person explanatory multidimensional item response theory with the instrument package in R.

We present the new R package instrument to perform Bayesian estimation of person explanatory multidimensional item response theory. The package implements an exploratory multidimensional item response theory model and a higher-order multidimensional item response theory model, a type of confirmatory multidimensional item response theory. Explanation of person parameters is accomplished by fixed and random effect linear regression models. Estimation is carried out using Hamiltonian Monte Carlo in Stan. In this article, we provide a detailed description of the models; we use the instrument package to demonstrate fitting explanatory item response models with fixed and random effects (i.e., mixed modeling) of person parameters in R; and, we perform a simulation study to evaluate the performance of our implementation of the models.

Digital Developmental Advising Systems for Engineering Students Based on Accreditation Board of Engineering and Technology Student Outcome Evaluations

The purpose of this research is to examine the benefits and limitations of the implementation of novel digital academic advising systems based on the principles of authentic outcome-based education (OBE) using automated collection and reporting processes for Accreditation Board for Engineering and Technology (ABET) student outcomes data for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools®, including an advising module based on assessment methodology incorporating the faculty course assessment report, performance indicators, and hybrid rubrics classified according to the affective, cognitive, and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of a) detailed students’ outcomes and performance indicator information and b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the faculty course assessment report using performance indicators and hybrid rubrics can provide comprehensive and realistic outcome data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions, and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better-performing outcomes to reinforce their natural affinity for learning specialized competencies to help them pursue related and successful career paths.

Assessment of Stigma among Caregivers of Armed Forces Professionals Suffering from Mental Illness

Abstract Background: Having a person with mental illness in the family invariably leads to stigmatic experiences to the family members. Perceived stigma in caregivers leads to social isolation, psychological distress, and suboptimal functioning. Additionally, perceived stigma among such caregivers is one of the barriers that prevent them from taking appropriate care of their dependents with mental illness. There has been little research in this field in the Indian armed forces population. Objective: The objective of this study was to assess the perceived stigma among caregivers of armed forces personnel suffering from mental illnesses. Methods: The calculated sample size was 139 but in our study period of time (December 1, 2019–February 28, 2020) we received the enough number of patients and we continued our study with 150 study subjects which is enough more than the 139. Hence, the total number of 150 caregivers visiting the psychiatry outpatient department of a tertiary care military hospital in North India along with their mentally ill dependents were included in the study after obtaining valid informed consent. The subjects were interviewed, their sociodemographic data were collected, and stigma scores were assessed on a 15-item Explanatory Model Interview Catalogue (EMIC) Stigma Scale. Results: Total stigma score ranged from 20 to 40 with a median of 31. Caregivers with lower level of education, lower income, rural residency, elder age, and diagnosis of psychosis/substance abuse in dependent were found to have higher scores of perceived stigma. Conclusions: Caregivers of patients with mental illnesses experience significant stigma which needs to be addressed so that they can provide optimum care to their mentally ill dependents. Limitation: As EMIC does not provide any standard cutoff for low or high stigma or to categorize persons as having stigma or not, it was difficult to compare results with other studies that used different tools to assess stigma.

Interpretative structural modeling method: An analysis of influencing factors analysis on the adverse experiences of cardiac surgery patients in the intensive care units

ObjectiveTo construct an interpretation structure model of adverse experiences of cardiac surgery patients in intensive care unit, so as to provide a reference for optimizing the experience of critical patients step by step. MethodsLiterature review, semi-structured interviews, questionnaires and Delphi method were used to summarize and analyze the influencing factors of intensive care experience in cardiac surgery. The explanatory structural model was used to divide the influencing factors into levels and construct the explanatory structural model of adverse experience of cardiac surgery patients in intensive care. ResultsA hierarchical structure model containing 34 elements and 15 levels was constructed, which were divided into Surface level, middle level and root level. ConclusionThe intensive care experience of patients in cardiac surgery department is mainly affected by 34 factors. There are direct or indirect correlations between the influencing factors, and different levels have different effects.

Ada-XG-CatBoost: A Combined Forecasting Model for Gross Ecosystem Product (GEP) Prediction

The degradation of the ecosystem and the loss of natural capital have seriously threatened the sustainable development of human society and economy. Currently, most research on Gross Ecosystem Product (GEP) is based on statistical modeling methods, which face challenges such as high modeling difficulty, high costs, and inaccurate quantitative methods. However, machine learning models are characterized by high efficiency, fewer parameters, and higher accuracy. Despite these advantages, their application in GEP research is not widespread, particularly in the area of combined machine learning models. This paper includes both a GEP combination model and an explanatory analysis model. This paper is the first to propose a combined GEP prediction model called Ada-XGBoost-CatBoost (Ada-XG-CatBoost), which integrates the Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost) algorithms, and SHapley Additive exPlanations (SHAP) model. This approach overcomes the limitations of single-model evaluations and aims to address the current issues of inaccurate and incomplete GEP assessments. It provides new guidance and methods for enhancing the value of ecosystem services and achieving regional sustainable development. Based on the actual ecological data of a national city, data preprocessing and feature correlation analysis are carried out using XGBoost and CatBoost algorithms, AdaGrad optimization algorithm, and the Bayesian hyperparameter optimization method. By selecting the 11 factors that predominantly influence GEP, training the model using these selected feature datasets, and optimizing the Bayesian parameters, the error gradient is then updated to adjust the weights, achieving a combination model that minimizes errors. This approach reduces the risk of overfitting in individual models and enhances the predictive accuracy and interpretability of the model. The results indicate that the mean squared error (MSE) of the Ada-XG-CatBoost model is reduced by 65% and 70% compared to the XGBoost and CatBoost, respectively. Additionally, the mean absolute error (MAE) is reduced by 4.1% and 42.6%, respectively. Overall, the Ada-XG-CatBoost combination model has a more accurate and stable predictive performance, providing a more accurate, efficient, and reliable reference for the sustainable development of the ecological industry.

A Systematic Review of Parental Self-Efficacy in Parents of Autistic Children.

Parental self-efficacy (PSE) assesses a parent's expectations and beliefs about their ability to effectively parent their child. PSE has implications for a parent's well-being, parenting practices, mental health, the parent-child relationship, and child adjustment. While PSE has been extensively examined within the broader parenting literature, the examination of PSE specifically for parents of autistic children has gained increasing attention in recent years. The following systematic review aimed to investigate the role of PSE for parents of autistic children by examining variables that predict PSE or are predicted by PSE in relation to how they align with the broader parenting literature and are unique to autism. Utilizing PRISMA guidelines, peer-reviewed articles were included if (a) participants included caregivers of autistic children, (b) at least one quantitative outcome measure of PSE was utilized, and (c) the role of PSE was examined as an outcome, predictor, or variable in an explanatory model. A total of 53 studies were included in the review and the role of PSE was examined regarding family (e.g., parental characteristics, parent stress, well-being, and support) and child factors (e.g., autism symptomology, problem behaviors, interventions). Several themes emerged including a positive relationship between PSE and support, and a negative relationship between PSE and parenting stress, parent mental health outcomes (e.g., anxiety, depression), and autism symptomology. Findings were compared to the broader parenting and PSE literature to examine how increased considerations and challenges (e.g., child problem behaviors, social impairment, and caregiver strain) associated with raising an autistic child might impact PSE.

Subjective Cognitive Dysfunction in Chronic Illness: A Systematic Review and Meta-Synthesis.

Qualitative studies have examined the experiences of subjective cognitive dysfunction in specific populations or specific disease stages, but there has not yet been a systematic synthesis and evaluation of findings related to perceptions of subjective cognitive dysfunction in nondementia-related chronic illnesses. The aim of this study was 2-fold: (1) to undertake a systematic review of experiences of subjective cognitive dysfunction in people with nondementia-related chronic disease and (2) to develop an explanatory framework to describe the experiences of living with subjective cognitive dysfunction. Four databases were systematically searched for studies on subjective cognitive dysfunction up to June 2023. Qualitative synthesis was conducted on the final sample (N = 25) using Sandelowski's adaptation of Nobilt and Hare's reciprocal transactional analysis method. Critical appraisal was completed using the Critical Appraisal Skills Programme checklist. Through constant comparison of key concepts, findings were organized within 4 interrelated themes that informed a conceptual explanatory model of adapting to living with subjective cognitive dysfunction: (1) symptoms, (2) health care, (3) perceptions of self, and (4) relationships. Participants highlighted how subjective cognitive dysfunction affected interactions in health care settings and involved other symptoms that in turn complicated meaning, self-enhancement, and mastery. Our model of the process of adapting provides a new way to conceptualize cognitive dysfunction in chronic illness and suggests opportunities for health care professionals to support patients and their families. The results highlight the need for more research to better understand the role of subjective cognitive dysfunction in nondementia-related chronic illnesses.The review protocol was registered in PROSPERO (CRD42021231410).

Racial capitalism and firearm violence: Developing a theoretical framework for firearm violence research examining structural racism

Despite the early promise of centering structural racism in explanatory models of firearm violence, there are noticeable gaps in what's been produced thus far; in particular, a deeper and more serious engagement with long-standing theories of racism is needed to further enrich our understanding of how structural inequalities produce unequal burdens of firearm-related harms. Thus, building on theories and concepts from a range of academic fields and Black philosophical perspectives, we developed a theoretical framework to help explain the role of place-based structural racism on firearm violence disparities. A central component of our framework is the concept racial capitalism, which contends that racial exploitation and the accumulation of assets depend on and reinforce one another. In this article, we present our framework and highlight how two processes related to racial capitalism—racialized dispossession and racialized spatial stigma—are connected with geographic disparities in firearm violence. We also present the results of an ecological cross-sectional study that reveals a potential key association between racial capitalism and firearm violence disparities on the neighborhood-level. We used a structural intersectionality approach and descriptive epidemiological methods to highlight and quantitatively describe spatial firearm violence disparities that could potentially be linked to the varying exposure of two dimensions of racial capitalism—historical redlining and contemporary racialized subprime mortgage lending. We found that sustained disadvantaged census tracts (tracts that were historically redlined and experienced higher contemporary subprime lending) experienced the highest burden of firearm violence in Baltimore City between 2015 and 2019. Our research suggests that racial capitalism could potentially be a root cause of firearm violence disparities. A theoretical framework based on racial capitalism can inform the development and usage of indicators and analytic methods for racism-related firearm violence research. Moreover, this framework can identify factors to prioritize in equity-based violence prevention policies and programs.

Response processes for patients providing quantitative self-report data: a qualitative study.

To identify factors that influence response processes for patients providing quantitative self-report data. Secondly, due to the lack of integrative and explanatory models in this area, to develop a model of patients' response processes that can guide what to look for when considering validity evidence and interpreting scores on individual items. Participants (n = 13) were recruited from a specialized substance use disorder treatment clinic and interviewed while responding to items from a clinical feedback system implemented for routine outcome monitoring in that setting. The interview approach was based on cognitive interviewing. Data collection and analysis were inspired by a grounded theory approach. We identified several variables that influenced the participants' response processes. The variables were organized into five categories: context-related variables; item-related variables; response base variables; reasoning strategies; and response selection strategies. We also found that the participants' responses for many items were affected by different aspects of the response process in ways that are relevant to interpretation but not necessarily discernible from the numerical scores alone, and we developed response categories to capture this. The findings suggest that patients providing quantitative self-report data encounter conditions in the response process that challenge and influence their ability to convey meaning and accuracy. This results in responses that for many of the items reflect messages important for interpretation and follow-up, even if it does not appear from the numerical scores alone. The proposed model may be a useful tool when developing items, assessing validity, and interpreting responses.

Family Members' Explanatory Models of Cancer Anorexia-Cachexia.

The experience of bearing witness to the lack of appetite and involuntary weight loss that characterizes cancer anorexia-cachexia syndrome (CACS) is reported to be stressful for family members. Research identifies that family members engage in a wide range of behaviors in response to a relative who shows minimal interest in eating and is literally 'wasting away' before their eyes. Some families, though concerned about the symptoms of CACS, do not dwell excessively on the patient's nutritional intake while others continually harass the patient to eat and petition health care providers for aggressive nutritional interventions to eat in an attempt to stave off further physical deterioration. While studies have detailed how family members respond to a terminally ill relative with CACS, empirical work explicating the explanatory models of CACS that they hold is lacking. Explanatory models (EMs) reflect the beliefs and ideas that families have about why illness and symptoms occur, the extent to which they can be controlled, how they should be treated, and how interventions should be evaluated. To address this gap in the literature, a grounded theory study guided by Kleinman's Explanatory Model questions was conducted with 25 family members of advanced cancer patients. The core category of 'Wayfaring' integrates the key categories of the model and maps onto Kleinman's questions about CACS onset, etiology, natural course, physiological processes/anatomical structures involved, treatment, and the impacts of disease on patient and family. Findings suggest that a divergence between some biomedical constructions of CACS and explanatory models held by family members may fuel the family-health care provider conflict, thereby providing direction for communication with families about care of the patient with anorexia-cachexia.

Bridges between two medical realities: Perspectives of Indigenous medical and nursing students on snakebite care in the Brazilian Amazon.

In the Brazilian Amazon, snakebite envenomations (SBEs) disproportionately affect Indigenous populations, and have a significantly higher incidence and lethality than in non-Indigenous populations. This qualitative study describes the Indigenous and biomedical healthcare domains for SBE care from the perspective of the Indigenous medical and nursing students in Manaus, Western Brazilian Amazon. In-depth interviews were conducted with five Indigenous students from the Amazonas State University, between January and December 2021. The interviews were analyzed using inductive content analysis. We organized an explanatory model with five themes: (1) participants' identities; (2) causality levels in Indigenous and biomedical systems; (3) therapeutic itineraries in Indigenous and biomedical systems; (4) ideological implications of adding biomedical devices to Indigenous healing systems; and (5) therapeutic failure in and efficacy of Indigenous and biomedical systems. From a noncolonial perspective and seeking to increase the quality and acceptability of health care for the Indigenous populations of the Brazilian Amazon, the training of Indigenous health professionals presents itself as a promising strategy. For this goal, universities should serve as empowering settings for Indigenous health students that support them in their growth and development, raise their awareness of injustice, and catalyze change toward a culturally adapted and effective service for the users.

A predictive and explanatory model for remaining useful life of crushers using deep learning

A predictive and explanatory model for remaining useful life of crushers using deep learning

Suicide Risk Factors in High School Students.

In Mexico, suicide has become an important public health problem, representing the third leading cause of death in the adolescent population. Suicidal behavior in adolescents is associated with the interaction of complex relationships between personal, interpersonal, and sociocultural factors. Through a quantitative, descriptive, and correlational cross-sectional study, the present study aimed to analyze the prevalence and risk factors associated with suicidal ideation among adolescents from different high schools of the high school system (SEMS) of the University of Guadalajara, in response to the psychosocial impact of the COVID-19 pandemic. A descriptive statistical analysis was carried out on the data obtained from the 3583 students surveyed, followed by a principal component analysis (PCA) to identify closely related social, emotional, and behavioral variables. The PCA yielded eight principal components, which together represent 75.42% of the variance across psychometric tests. A multiple linear regression analysis was used, with a regression value (R2) of 0.4811, indicating that the explanatory model can predict 48.1% of the variability in suicidal ideation, with a statistical significance level of 0.05. According to the studies conducted, 19% (688 students) showed indicators of high suicide risk and 26.8% (960 students) showed moderate risk. Depression, mental health, health-related quality of life, physical and psychological well-being, and mood and emotions are the most influential factors in suicidal ideation.

Investigation of community knowledge, attitudes and stigma towards leprosy in Nigeria: a mixed-methods study.

Little is known about community knowledge and stigma towards leprosy in endemic settings. The aim of this study was to evaluate community knowledge, attitudes and stigma towards leprosy in Nigeria. This was a mixed-methods study consisting of a quantitative cross-sectional survey of community members and qualitative focus group discussions with community members and people affected by leprosy as well as key informant interviews with healthcare workers and community leaders. Of the 811 survey participants, 401 (49.4%) had a poor knowledge of leprosy that was driven by cultural beliefs, fear of its contagiousness and poor knowledge of its means of transmission. The participants reported high stigma levels with a mean score of 18.96±7.73 on the Explanatory Model Interview Catalogue Community Stigma Scale and 9.39±7.03 on the Social Distance Scale. Stigma levels were influenced by age, residence, education and knowledge of leprosy. Qualitative data suggested that community members were scared of leprosy infectiousness, and local illness concepts and misconceptions informed attitudes and behaviour towards leprosy in the community. Community members have a poor knowledge of, and high stigma levels towards leprosy. Culture-specific health education and behavioural change interventions are needed to address the identified gaps.

Fall of dwarfs: micro and macroeconomic determinants of the disappearance of European small banks

Based on a wide sample of banks headquartered in 27 European countries over the period 2005–2022, this paper tests the influence that microeconomic and macroeconomic variables have on the probability of small banks exiting the market, evaluating the predictive power of the explanatory models employed. Our approach to the determinants of small banks’ exit proves that even the macroeconomic and socio-demographic reference context can have a predictive effectiveness similar to that of the accounting variables, especially when contagion effects at the local level are taken into account.