Multilevel Approach Research Articles

Erratum: Agri vs. food? Perceptions of local policymakers on agri-food policies from a multilevel approach

Erratum: Agri vs. food? Perceptions of local policymakers on agri-food policies from a multilevel approach

From policies to outcomes: multi-level analysis of the influence of regional planning on land take

ABSTRACT Urban expansion poses significant challenges to sustainability and undermines efforts for efficient spatial development. This study examines the relationship between regional planning regulations, interest groups and contextual factors affecting residential land take in Germany. The study employs a multi-level regression approach, analysing 4032 municipalities nested in 91 planning regions and utilizes a nationwide dataset on changes in residential and mixed land use. The overall goal of the study is to examine the effect of regional planning regulations and local interest groups, within varying contextual factors, on residential land take. Results show a dampening effect of regional regulatory intensity on residential land take at the local level. However, the effect becomes non-significant when considering local and regional covariates, revealing that regional planning alone may not be sufficient to reduce residential land take effectively. Findings underscore the complexity of regional planning and the importance of understanding its impacts on the local and regional level. As this study focuses on residential land take, further research is required to analyse the effectiveness of regional planning in managing land uses like trade, industry and infrastructure, concentrating urban development in central places, promoting transit-oriented development and supporting higher residential densities.

Interactions between task complexity, task repetition, and task motivation in L2 writing

This study explores the interplay between task repetition and task motivation in second language (L2) writing, specifically focusing on syntactic complexity, accuracy, and lexical complexity (CAL). Aligning with Dörnyei’s call to balance cognitive and affective factors in task-based language teaching (TBLT), the research involved 100 advanced mid-level learners of English as a second language (ESL) aged between 18 and 20 years. Participants completed both simple and complex argumentative writing tasks in a counterbalanced order, with a one-week interval between tasks. A perception questionnaire was administered immediately after each task performance to validate cognitive task complexity manipulations. Subsequently, participants repeated the same tasks with the same time interval, complemented by the completion of a task motivation questionnaire after each task. Written essays were collected and analysed for CAL measures. Employing a multivariate multilevel approach, the results were triangulated with self-report data. Findings indicate that task repetition significantly impacted CAL in L2 writing, with a positive moderation effect by task complexity. Additionally, task motivation enhanced syntactic complexity in both simple and complex tasks, with negligible effects on accuracy. Notably, task motivation exerted a more substantial influence on students’ repeated task performance concerning lexical complexity. The study’s results offer theoretical and pedagogical insights for TBLT researchers and L2 writing practitioners.

Enhancing Metaphor Recognition of Literary Works in Applied Artificial Intelligence: A Multi-Level Approach with Bi-LSTM and CNN Fusion

ABSTRACT Understanding metaphorical language is essential for AI to interpret and communicate with humans accurately. However, current methods often struggle with the complexity of metaphors, making it difficult for AI systems to understand human language fully. Recognizing metaphors is challenging because they are frequently ambiguous and depend on context. In this study, we propose a new approach using a combination of Bi-directional Long Short-Term Memory (Bi-LSTM) networks, Convolutional Neural Networks (CNN), and uni-directional LSTM components to create a multi-level model for recognizing metaphors. Our model uses various features, including dependency, semantics, and part-of-speech, to improve its learning ability. Additionally, we introduce a new method for recognizing the emotional context of metaphors using a random walk model to determine the emotional tone of words. Our results show that this model improves performance in recognizing metaphors, enhancing AI’s ability to understand them.

A multi-level rescheduling approach for a dynamic remote operations scheduling problem

ABSTRACT In this paper, we tackle a dynamic scheduling problem faced by a large international company. The problem involves assigning installation projects arriving over time to specialised technicians who execute them remotely. Each project consists of several tasks having processing times, release dates, and execution deadlines. The company needs to assign projects to technicians and schedule tasks complying with technicians' skills, precedence constraints between tasks, and tasks requiring multiple technicians simultaneously. The problem is dynamic as new projects and tasks become available over time, requiring their allocation to technicians. We formulate the offline problem as a mixed integer linear program that minimises the makespan, and we address the dynamic version solving restricted problems within a rolling horizon framework. The approach systematically implements different levels of schedule adjustment to incorporate new information. To study scalability, we validate our algorithm by using both real-world and synthetic simulations demonstrating its efficiency and effectiveness. Additionally, we provide interesting managerial insights for the company.

Accounting Method and Indicators of Multilevel CO2 Emissions Based on Cost During Construction of Shield Tunnels

With the rapid expansion of shield tunnel projects in China, precise accounting of CO2 emissions throughout the entire process is essential for advancing green and low-carbon construction practices. This paper introduces an innovative CO2 accounting methodology utilizing a cost-based carbon emission coding system. It adopts a multilevel approach to carbon emission accounting, aligned with this coding system, which facilitates a detailed examination of carbon emission ratios and characteristics across various construction techniques. The analysis includes six typical shield tunnel projects varying in diameter, focusing on sub-projects to scrutinize CO2 emissions and establish specific indicators. The findings indicate that CO2 emissions per 10,000 yuan of investment, approximately 30.25% (or 3025 kgCO2e/10,000 yuan), are more consistent than those per unit of length. Moreover, the study highlights differing CO2 emission trends among sub-projects compared to whole tunnel projects, assessing emission indicators and distribution patterns in four sub-projects: shield excavation, segmental lining, internal structure, and tunnel reinforcement. From these findings, the paper suggests more precise and tailored strategies for CO2 reduction. This research provides a theoretical basis for future construction planning and carbon management strategies.

Multi-level attention graph feature fusion smooth prognostics approach for aircraft engines remaining useful life prediction

In the development of a prognostic and health management system, it is a challenge to mine the degradation characteristics, estimate the RUL reliably and smoothly, and explain the RUL prediction results from the multi-sensor condition monitoring data collected under the non-fixed-length equipment operation cycle and multi-operating conditions. In this paper, a Multi-level Attention Graph feature Fusion Smooth Prognostics Approach (MAGF-SPA) is proposed to achieve accurate, smooth, and interpretable RUL prediction results. In this method, firstly, multivariate time series (MTS) are transformed into graph signals from a purely data-driven perspective. Secondly, the four-level feature extraction strategy, which includes node-level, subgraph-level, cyclical-level, and temporal-level feature extraction, is designed to extract the inherent hidden degradation characteristics of MTS. Finally, a smoothing trick proposed in this paper can optimise the RUL prediction value corresponding to the degradation characteristics in real time. Experimental results show that the proposed method achieves state-of-the-art on the N-CMAPSS dataset.

The impact of emotion regulation on the establishment of the therapeutic alliance in post-divorce group intervention: A multilevel approach.

The present study focuses on the impact of emotion regulation on the establishment of the therapeutic alliance in a context of post-divorce group intervention. The study involved 177 divorced parents and 60 therapists and was developed through multilevel path analysis. The data showed an association between emotion regulation and therapeutic alliance across the difficulties of adaptation to divorce, although the results were different from the perspective of the participant and of the therapist and between the individual and the group level. The study highlights the relevant role of emotion regulation in post-divorce adaptation and in the success of the establishment of the therapeutic alliance, while clarifying the formation of the alliance from the viewpoints of the individual and the therapist. The study also highlights the need to understand the alliance at both the individual and the group level, in order to design therapeutic interventions.

International survey of people living with chronic conditions (PaRIS survey): effects of general practitioner non-participation on the representativeness of the Norwegian patient data

BackgroundThe International Survey of People living with Chronic Conditions (OECD-PaRIS survey), aims to systematically gather patient-reported experiences (PREMs) and - outcomes (PROMs) and potential predictors for these outcomes for persons with chronic conditions as well as information from professionals about health care provided. In such patient surveys, the advantages of a multilevel (nested) approach in which patients are sampled ‘within providers’ need to be balanced against the potential for bias if patient populations from participating GPs significantly differ from those of non-participating GPs. The objective was to assess the effects of general practitioner (GP) non-participation on the representativeness of the Norwegian patient data of the International Survey of People living with Chronic Conditions (OECD-PaRIS survey).MethodsTo test all aspects of the first main PaRIS survey, it was preceded by a field trial which this paper reports on the Norwegian part of. For the Norwegian part of the field trial in 2022, we randomly sampled and surveyed 75 GPs and 125 patients 45 years and older for each GP, regardless of whether their GP were also participating in the study. GPs were sampled from a national register that included all GPs. The surveys were primarily digital, but we sent postal questionnaires to non-digital patients and non-responding digital patients. We compared GP and patient characteristics as well as patient-reported experiences and outcomes according to GP participation status in bivariate analysis, supplemented with multiple linear regressions with PREMs/PROMs as dependent variables and participation status as independent adjusting for significant patient factors.Results17 of 75 sampled GPs participated (22.7%), of which 993 of 2,015 patients responded (49.3%). 3,347 of 7,080 patients of non-responding GPs answered (47.3%). Persons with chronic conditions from participating GPs reported significantly better patient-centred coordinated care (p = 0.017), overall experiences with the GP office the last 12 months (p = 0.004), mental well-being (p = 0.039) and mental health (p = 0.013) than patients from non-participating GPs. The raw differences between participating and non-participating GPs on patient-reported experiences and – outcomes varied from 1.5 to 2.9 points on a 0-100 scale, and from 2.2 to 3.0 after adjustment for case-mix.ConclusionsThe Norwegian field trial indicates that estimates based on participants in the PaRIS survey may modestly overestimate patient-reported experiences and -outcomes at the aggregated level and the need for more research within and across countries to identify and address this potential bias.

Genomic Exploration of Essential Hypertension in African-Brazilian Quilombo Populations: A Comprehensive Approach with Pedigree Analysis and Family-Based Association Studies.

Essential Hypertension (EH) is a global health issue. Despite extensive research, much of EH heritability remains unexplained. We investigated the genetic basis of EH in African-derived individuals from partially isolated quilombo populations in Vale do Ribeira (SP-Brazil). Samples from 431 individuals (167 affected, 261 unaffected, 3 unknown) were genotyped using a 650k SNP array. Estimated global ancestry proportions were 47% African, 36% European, and 16% Native American. We constructed six pedigrees using additional data from 673 individuals and created three non-overlapping SNP subpanels. We phased haplotypes and performed local ancestry analysis to account for admixture. Genome-wide linkage analysis (GWLA) and fine-mapping via family-based association studies (FBAS) were conducted, prioritizing EH-associated genes through systematic approach involving databases like PubMed, ClinVar, and GWAS Catalog. Linkage analysis identified 22 regions of interest (ROIs) with LOD scores ranging 1.45-3.03, encompassing 2363 genes. Fine-mapping (FBAS) identified 60 EH-related candidate genes and 117 suggestive/significant variants. Among these, 14 genes, including PHGDH , S100A10 , MFN2 , and RYR2 , were strongly related to hypertension harboring 29 suggestive/significant SNPs. Through a complementary approach - combining admixture-adjusted GWLA based on Markov chain Monte Carlo methods, FBAS on known and imputed data, and gene prioritizing - new loci, variants, and candidate genes were identified. These findings provide targets for future research, replication in other populations, facilitate personalized treatments, and improve public health towards African-derived underrepresented populations. Limitations include restricted SNP coverage, self-reported pedigree data, and lack of available EH genomic studies on admixed populations for independent validation, despite the performed genetic correlation analyses using summary statistics.

External Factors Influencing the Implementation of Policy, System, and Environmental Change Strategies Within Cooperative Extension.

Introduction. Multilevel approaches are important to promote the adoption of healthier practices at the individual and community levels. Cooperative Extension pairs policy, systems, and environmental (PSE) change approaches with direct education programs focused on individual and community health. This study sought to understand Family and Consumer Sciences (FCS) Extension agents' perceptions of PSE change supports, benefits for their communities, and resources and partnerships important for implementation. Method. The Consolidated Framework for Implementation Research was used to develop a survey to assess outer setting domains related to PSE work. In addition to close-ended questions, the survey included several open-ended, qualitative questions exploring the benefits, resources, and relationships required to implement PSE changes. The surveys were collected online from FCS agents in two states. Descriptive statistics were calculated, open responses were coded, and key themes were established. Results. FCS agents (n=116) indicated high levels of agreement about having necessary support from state-level specialists (71%) and the overall Extension organization (64%). The largest gaps in support were reported as grant or external funding sources, time, and county programming funds. Half of the sample agreed that PSE training was adequate, but only 38.5% reported previously participating in PSE-specific training. Open-ended question responses revealed diverse relationships and numerous benefits of PSE work, including more sustainable health behavior changes and greater local Extension visibility. Discussion. Our findings corroborate previous work and identify potential gaps that future interventions can address to better support Extension and public health professionals when implementing PSE work at the community level.

Analyzing women’s knowledge of pregnancy complications in Ethiopia through a multilevel approach

Pregnancy complications can’t always be predicted. However, pregnant women must be aware of these pregnancy complications to avoid serious complications and begin treatment as soon as possible. Maternal mortality can be decreased by offering high-quality maternity services and educating women about the early warning signs of pregnancy. Therefore, the current study aimed to assess individual and community-level factors associated with women’s knowledge about pregnancy complications in Ethiopia based on the 2019 Ethiopian mini-demographic health survey data (EMDHS). The study analyzed data from the 2019 EMDHS, including a weighted sample of 1,655 reproductive-age women. Multilevel binary logistic regression analysis was used to fit the associated variables. Using the interclass correlation (ICC), deviance, proportional change variance (PVC), and median odds ratio (MOR), the comparison and fit of the models were evaluated. The significant variables associated with knowledge about complications during pregnancy were identified using an adjusted odds ratio (AOR) and a 95% confidence interval (CI). The proportion of mothers with good knowledge of pregnancy complications was 44.8% (CI 42.4%–47.2%). The multi-level analysis revealed that secondary education (AOR = 1.54, 95% CI = 1.04–2.29), a higher education level (AOR = 1.74, 95% CI = 1.11–2.72), four and above ANC Visits (AOR = 0.74, 95% CI = 0.49–0.98), women who lived in Amhara (AOR = 2.10, 95% CI = 1.24–3.55), and SNNPR (AOR = 3.92, 95% CI = 2.10–7.31) were positively associated with knowledge about pregnancy complications while, women residing in Harari (AOR = 0.20, 95% CI = 0.98–0.44) and Dire-Dawa (AOR = 0.48, 95% CI = 0.24–0.95) were negatively associated with knowledge about pregnancy complications. This study found that nearly half (44.8%) of the study participants demonstrated knowledge about pregnancy complications. This suggests a significant gap in awareness that could potentially impact access to obstetric care for women experiencing complications during pregnancy. Therefore, prioritizing enhancements in antenatal counseling services and community health education regarding pregnancy complications is crucial.

Computational Multiscale Study of the Interaction Between the PDMS Polymer and Sunscreen-Related Pollutant Molecules

Sunscreen molecules play a critical role in protecting skin from ultraviolet radiation, yet their efficient detection and separation pose challenges in environmental and analytical contexts. In this work, we employ a multilevel modeling approach to investigate the molecular interactions between representative sunscreen molecules and the polydimethylsiloxane (PDMS) polymer, a material widely recognized for its sorbent properties. Our goal is to explore how these interactions can be fine-tuned to facilitate the effective separation of sunscreen molecules in portable membrane inlet mass spectrometry (MIMS) systems, potentially leading to the development of new membrane materials. Using a combination of advanced computational techniques—force field molecular dynamics simulations, semiempirical GFN2-xTB, and density functional theory calculations—we assess the interaction strength and noncovalent interactions of sunscreen molecules, namely oxybenzone, naphthalene, benzo[a]anthracene, avobenzone, and 1,3,5-trichlorobenzene, with PDMS. Additionally, the effect of temperature on the interaction dynamics is evaluated, with the aim of extending the sorbent capacities of PDMS beyond light polar molecules to larger, polar sunscreen compounds. This study provides critical insights into the molecular-level interactions that may guide the design of novel membrane materials for efficient molecular separation.

Investigating the links between questionable research practices, scientific norms and organisational culture

BackgroundThis study investigates the determinants of engagement in questionable research practices (QRPs), focusing on both individual-level factors (such as scholarly field, commitment to scientific norms, gender, contract type, and career stage) and institution-level factors (including industry type, researchers' perceptions of their research culture, and awareness of institutional policies on research integrity).MethodsUsing a multi-level modelling approach, we analyse data from an international survey of researchers working across disciplinary fields to estimate the effect of these factors on QRP engagement.ResultsOur findings indicate that contract type, career stage, academic field, adherence to scientific norms and gender significantly predict QRP engagement. At the institution level, factors such as being outside of a collegial culture and experiencing harmful publication pressure, and the presence of safeguards against integrity breaches have small associations. Only a minimal amount of variance in QRP engagement is attributable to differences between institutions and countries.ConclusionsWe discuss the implications of these findings for developing effective interventions to reduce QRPs, highlighting the importance of addressing both individual and institutional factors in efforts to foster research integrity.

Water diplomacy in the pursuit of water security: A multilevel approach

Water diplomacy in the pursuit of water security: A multilevel approach

Examining the structure of the revised community of inquiry framework: a multi-level approach

Examining the structure of the revised community of inquiry framework: a multi-level approach

Seeing adolescents grow from many angles using a multilevel approach: A tribute to the contributions of Dante Cicchetti to the field of developmental psychopathology.

Dante Cicchetti propelled forward the field of developmental psychopathology by advancing this framework and championing new methods, including emphasizing the central role that multilevel analysis holds for explicating pathways of risk and resilience. His work continues to change the face of existing science. It has also paved the way for the formation of new projects, like the Research Domain Criteria initiative. This paper uses our laboratory's work on multilevel approaches to studying adolescent depression, non-suicidal self-injury, and suicidal thoughts and behaviors to shine a spotlight on Dr Cicchetti's contributions. In addition, we review recent developments, ongoing challenges, and promising future directions within developmental psychopathology as we endeavor to carry on the tradition of growth in the field.

Social determinants of breast cancer screening: a multilevel analysis of proximal and distal factors related to the practice of mammography.

Mammography is crucial for early breast cancer detection. In Latin America, Argentina faces a significant breast cancer burden, with varying mammography rates. The social factors influencing mammography practices remain unclear. This study aimed to identify the proximal and distal social determinants of this practice among Argentinean women using a multilevel approach. This nationwide cross-sectional study included 4,924 women aged 50-70 participating in the 2018 National Risk Factor Survey of Argentina. Two-level logistic models were used to estimate measures of association (ORs) between timely mammography practice (within the last 2 years) and selected covariates (sociodemographics, proximal environment, and distal-level variables). The intraclass correlation coefficient (ICC) and proportional change in variance (PCV) were calculated. 62.8% of women underwent timely mammography. Age (OR=0.96; 95%CI 0.94-0.97), health insurance (OR=2.22; 95%CI 1.87-2.63), education (OR=2.1; 95%CI 1.74-2.64), and income (OR=1.56; 95%CI 1.23-1.97) were associated with mammography practice. Women in non-marital (OR=0.61; 95%CI 0.52-0.72) or larger households (OR=0.61; 95%CI 0.51-0.63) were less likely to have timely mammograph; living in a larger city was positively associated (OR=1.28; 95%CI 1.12-1.46). Women in provinces with higher physician density (OR=1.06; 95%CI 1.01-1.11) and lower maternal mortality ratio (OR=0.9; 95%CI 0.87-0.96) had higher chances of timely mammography. The ICC and PCV suggested that the explored healthcare indicators largely explained the macro-contextual effect. Multilevel factors influenced mammography practices in Argentina. The results highlight disparities linked to sociodemographic characteristics and healthcare resources. Efforts to address social inequalities in breast cancer screening must consider multilevel determinants, including in healthcare settings.

When does the energy transition impact household affordability? A mixed-methods comparison of fourteen coal and carbon-intensive regions

Understanding what conditions promote or hinder energy affordability in energy transitions is crucial for coal and carbon-intensive regions (CCIRs) dealing with the trade-off between phasing out fossil fuels and deepening social inequalities. While previous studies have included household and national-level conditions, this paper addresses the research gap covering regional-level conditions by drawing from regional energy governance, energy justice, and sociotechnical transition frameworks. A mixed-method approach consisting of a fuzzy-set qualitative comparative analysis and case-study analysis is applied to 14 CCIRs in Europe, Asia, and North America. Results show that energy affordability in CCIRs is influenced by combinations of regional and (inter)national conditions. Whereas the existing literature and transition policies do not differentiate between the CCI sector’s transition type, this paper highlights that conditions underlying energy (un)affordability differ when the CCI sector is phased out or has the option to transition. Based on the findings, this study calls for a multi-level governance approach to alleviating and preventing energy unaffordability and recommends that policy mixes like the EU Just Transition Fund consider the different types of CCIR transitions.

Impact of COVID-19 Pandemic on Emergency Department Visits for Opioid Use Disorder Across University of California Health Centers

Introduction: Coronavirus 2019 (COVID-19) has had a devastating impact on mental health and access to addiction treatment in the United States, including in California, which resulted in the highest rates of emergency department visits (ED) for opioid poisoning in 2020. As California slowly returns to pre-pandemic normalcy, it remains uncertain whether the rates of opioid-related events have slowed down over time. We hypothesized that the number of opioid-related ED visits were exacerbated after the period of the COVID-19 pandemic and continue at a high rate in the present. Methods: In this analysis we searched the University of California (UC) Health Data Warehouse—a database of electronic health records from six UC health centers—for opioid-related ED visits, identifiying using the following International Classification of Diseases, 10th Ed, Clinical Modification codes: F11 codes, and T40.0*, T40.1*, T40.2*, T40.3*, T40.4*, T40.6*. Opioid overdose-associated visits were classified by types of opioids involved: heroin (T40.1*); prescription opioids (T40.2* or T40.3*); and synthetic opioids (T40.4*). We performed interrupted time analysis to estimate the immediate (level) change and change-in-time trend (trend change), from before (January 2018–October 2019) and during the pandemic (April 2020–December 2022). Monthly visit rates were evaluated with negative binomial regression adjusted for first-order autoregression and using all-cause ED counts as the offset. We present effect sizes as rate ratios (RR) and 95% confidence intervals (CI), tested at α = .05. Results: Before COVID-19, a steadily increasing trend in ED visit rates was observed for all outcomes (P < 0.05) except synthetic opioids. Total opioid-related ED visit rates increased by 15% (RR 1.15, 95%CI 1.02–1.29, P = 0.20) immediately after March 2020 before decreasing by 0.5% every month, albeit without statistical significance (RR .995, 95% CI .991–1.00, P = 0.06). Similar trends were observed with prescription opioid overdoses, with a step increase of 44% (RR 1.44, 95% CI 1.10–1.89, P = .008) before plateauing after March 2020 (RR 1.01, 95% CI .998–1.02, P = 0.12). After March 2020, ED visit rates for synthetic opioid overdoses were increasing steadily by 4% every month (RR 1.04, 95% CI 1.02–1.06, P = .001), unlike with heroin, which was observed with an 8% monthly reduction (RR .92, 95% CI .90–.93, P < .001). No immediate increase in visit rates was observed for either opioid. Conclusion: While opioid-related ED admissions among the UC health centers showed an overall decrease, prescription and synthetic opioid overdoses remained significantly higher than pre-pandemic trends as of December 2022. A multilevel approach to improve awareness of new opioid health policies could ameliorate these alarming rises in the post-pandemic era.