Saskatchewan Polytechnic

Achieving sustainable competitive advantage has become increasingly complex amid intense global environmental pressures. While leadership is widely recognized as an enabler of competitiveness, little is known about how top management's green inclusive leadership fosters sustainable competitive advantage through dynamic organizational capabilities such as green shared vision and green mindfulness. Drawing on the natural resource-based view, we theorize that top management's green inclusive leadership strengthens sustainable competitive advantage in manufacturing firms through these capabilities and that internal corporate social responsibility communication reinforces these effects. Data were collected in two waves from a purposive sample of 215 middle-level managers working in Pakistan's manufacturing sector and analyzed using PROCESS macro models in SPSS. We found that green inclusive leadership directly predicts sustainable competitive advantage, and that green mindfulness, but not green shared vision, mediates this relationship. We also identify a sequential pathway in which green inclusive leadership enhances green shared vision, which subsequently fosters green mindfulness, thereby improving sustainable competitive advantage. Crucially, this sequential mediation is stronger when internal corporate social responsibility communication is high, underscoring the role of communication as a strategic organizational capability. By integrating leadership, vision, mindfulness, and communication, this study advances the natural resource-based view and demonstrates how sustainability messages shared within organizations deepen employee engagement with leadership-driven environmental strategies.

Abstract This study investigates the dispositional tendencies of large language models(LLMs) by developing and validating a survey instrument tailored to their uniquecharacteristics. While prior research has adapted human psychometric frameworkssuch as the Big Five to LLMs, these approaches face conceptual andmethodological limitations. To address this gap, this study introduced three traits(convergent-divergent reasoning, novel-conventional problem solving, and ethicalorientation) that more directly capture the functional dispositions of LLMs.A forced-choice questionnaire was co-designed with multiple models to assessthese traits, and its predictive validity was tested against open-ended behavioraltasks. Results demonstrate that questionnaire responses align with observed tendencies,with moderate-to-strong correlations for divergent and novel reasoning,and consistent expression of virtue ethics across models. These findings suggestthat LLMs exhibit measurable, context-dependent dispositions that can be systematicallyassessed. The proposed framework contributes to ongoing efforts tointerpret model behavior, inform prompt design, and advance the theoreticaldiscourse on personality-like constructs in artificial systems.

BackgroundInternal displacement due to conflict or crises has led to millions, including children under five, residing in precarious internally displaced persons (IDP) camps. Malnutrition, predominantly undernutrition in Sub-Saharan Africa, is a significant concern in these contexts. This review aimed to assess the factors and determinants of malnutrition among under-five children in IDP Camps in Africa.MethodThe review was conducted according to the PRISMA guidelines, and the research protocol was registered in PROSPERO (CRD42023460266). Embase, MEDLINE, CINAHL, Web of Science, CABI s, Scopus and Google Scholar were searched methodically. The AXIS Critical Appraisal of Cross-sectional Studies Tool was utilized to evaluate the quality and risk of bias of each included study.ResultAfter screening full-text articles, eight studies meeting eligibility criteria were included in the review. Factors influencing nutritional status of under-five children were summarized into 11 themes: Age, Gender, Caregiver’s Educational Status, Family Planning, Antenatal Care, Displacement Status, Access to Safe Water and Handwashing, Dietary Diversity, Disease History, Caregiver’s Occupation, and Household Income.ConclusionThis review examined the factors contributing to malnutrition among under-five children in IDP camps in Africa, and sheds light on neglected crises. It adds to existing knowledge in this area. The findings underscore the need for future research to explore variations in malnutrition determinants across different IDP contexts and to focus on multi-sector collaboration for effective policy implementation in addressing malnutrition in African IDP camps.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40795-025-01131-0.

Abstract Critical Generative Artificial Intelligence (GenAI) literacy cannot be reduced to a universal framework. Rather, it must be understood as a constellation of situated literacies, shaped by disciplinary perspectives, socio-political contexts, and technological affordances. This multi-authored article explores the emerging concept of critical GenAI literacy and its postdigital configurations. Fourteen authors contributed with different sections, followed by five author-reviewers who examined the article as a whole. The objective was to invite diverse perspectives, constructive critique, and promote collaborative efforts to develop a clearer and more inclusive understanding of critical GenAI literacy. The introduction focuses on establishing some initial assumptions about what we mean by ‘critical’ in general, and for GenAI literacy in particular, and why a conjunction of postdigital configurations of the construct emerges as an adequate methodological solution. In the main part of the article, some authors focus on the concept of ‘literacies’ as a starting point for their reflection, while others directly examine critical GenAI literacy through a postdigital perspective. We conclude that critical GenAI literacy requires moving beyond technical skills to engage with AI’s epistemological, ethical, and relational dimensions, ensuring learners critically interrogate its role in knowledge production. Future inquiry should focus on integrating this literacy into education in ways that promote social justice, epistemic diversity, and democratic participation.

X-ray imaging remains the primary diagnostic tool for identifying bone fractures. Accurate classification of bone fractures from X-ray images is a critical task in the field of orthopedic diagnosis and treatment. However, this task poses several challenges due to the complex and variable nature of fracture patterns, as well as the need to consider the temporal progression of fracture healing. Traditional convolutional neural network (CNN) architectures, while effective in extracting spatial features from X-ray images, may not be sufficient to capture the dynamic and sequential aspects of fracture healing. In this work, we propose an improved Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture to address the limitations of CNN-only models in bone fracture classification. The proposed method aims to leverage the strengths of both CNNs and LSTMs to enhance the classification performance. The CNN component of the network is responsible for extracting relevant visual features from the X-ray images, while the LSTM layers are used to model the temporal relationships between these features, which are crucial for understanding the progression of fracture healing. Experimental results on a dataset of X-ray images of bone fractures demonstrate the superior performance of the CNN-LSTM architecture compared to traditional CNN-based approaches with 97.33%

Although culturally appropriate care is vital to quality of care, many barriers exist to implementing culturally appropriate care in practice. Nurse leaders are in a position where they can act toward addressing some of the barriers and engage nursing staff in strategies to promote the implementation of culturally appropriate care practices on a unit. This article is an opinion piece wherein the authors illustrate leadership strategies that advocate for and nurture a practice where nursing staff are supported to apply their culturally appropriate skills in practice.

This paper works with functions defined in metric spaces and takes values in complete paranormed vector spaces or in Banach spaces, and proves some necessary and sufficient conditions for weak convergence of probability measures. Our main result is as follows: Let X be a complete paranormed vector space and Ω an arbitrary metric space, then a sequence {μn} of probability measures is weakly convergent to a probability measure μ if and only if limn→∞∫Ωg(s)dμn=∫Ωg(s)dμ for every bounded continuous function g: Ω → X. A special case is as the following: if X is a Banach space, Ω an arbitrary metric space, then {μn} is weakly convergent to μ if and only if limn→∞∫Ωg(s)dμn=∫Ωg(s)dμ for every bounded continuous function g: Ω → X. Our theorems and corollaries in the article modified or generalized some recent results regarding the convergence of sequences of measures.

Traumatic brain injury (TBI) is a leading cause of death and disability, primarily caused by falls and motor vehicle collisions (MVCs). Although many TBIs are preventable, there is a notable lack of studies exploring the association of geographically defined TBI hotspots with social deprivation. Geographic information systems (GIS) can be used to identify at-risk neighborhoods (hotspots) for targeted interventions. This study aims to determine the spatial distribution of TBI by major causes and to explore the sociodemographic and economic characteristics of TBI hotspots and cold spots in Nova Scotia. Patient data for TBIs from 2003 to 2019 were obtained from the Nova Scotia Trauma Registry. Residential postal codes were geocoded and assigned to dissemination areas (DA). Area-based risk factors and deprivation status (residential instability [RI], economic dependency [ED], ethnocultural composition [EC], and situational vulnerability [SV]) from the national census data were linked to DAs. Spatial autocorrelation was assessed using Moran's I, and hotspot analysis was performed using Getis-Ord Gi* statistic. Differences in risk factors between hot and cold spots were evaluated using the Mann-Whitney U test for numerical variables and the χ2 test or Fisher's exact test for categorical variables. A total of 5394 TBI patients were eligible for inclusion in the study. The distribution of hotspots for falls exhibited no significant difference between urban and rural areas (p = 0.71). Conversely, hotspots related to violence were predominantly urban (p = 0.001), whereas hotspots for MVCs were mostly rural (p < 0.001). Distinct dimensions of deprivation were associated with falls, MVCs, and violent hotspots. Fall hotspots were significantly associated with areas characterized by higher RI (p < 0.001) and greater ethnocultural diversity (p < 0.001). Conversely, the same domains exhibited an inverse relationship with MVC hotspots; areas with low RI and ethnic homogeneity displayed a higher proportion of MVC hotspots. ED and SV exhibited a strong gradient with MVC hotspots; the most deprived quintiles displayed the highest proportion of MVC hotspots compared with cold spots (ED; p = 0.002, SV; p < 0.001). Areas with the highest levels of ethnocultural diversity were found to have a significantly higher proportion of violence-related hotspots than cold spots (p = 0.005). This study offers two significant contributions to spatial epidemiology. First, it demonstrates the distribution of TBI hotspots by major injury causes using the smallest available geographical unit. Second, we disentangle the various pathways through which deprivation impacts the risk of main mechanisms of TBI. These findings provide valuable insights for public health officials to design targeted injury prevention strategies in high-risk areas.

The current focus on the critical shortage of nurses puts nurses at risk for representation as a health workforce commodity, rather than being recognized as an autonomous, knowledge-based profession without which the health of Canada’s population is in jeopardy. Nurses’ knowledge and professional role includes evaluation of their career, working conditions, and impact on patients and populations of care. The concern that nurses are leaving the profession can be analyzed within a feminist perspective related to autonomy and advocacy. The focus of this reflective commentary is to highlight, at a personal and collective level, that nurses are witnesses to the conditions fueling nursing shortages, the impact on the practice environment and the structural factors influencing these areas. We are therefore compelled to articulate the seriousness of our concerns and assert our knowledge and value as a profession.

The acoustic and material properties of asphalt crumb rubber (ARC) pavement have been measured and correlated. Laboratory measurements revealed nonlinear relationships between the sound absorption coefficients and material properties of compacted specimens as a function of the percentage of crumb rubber content in conventional and modified binders. The acoustic performance of conventional asphalt and asphalt rubber concrete specimens are compared to their material properties as a function of crumb rubber content to determine how crumb rubber content influences sound absorption. The normal incidence sound absorption coefficients, porosity, percentage of voids in mineral aggregate, and absolute gas permeability are measured for specimens containing 0, 5, 10, 15, and 20% crumb rubber in the asphalt binder. The random incidence sound absorption coefficient and material property data share nonlinear distributions within some frequency ranges, as a function of crumb rubber content in the conventional and modified asphalt binders.