Explore the vast range of titles available.

Exposure to trauma is pervasive in societies worldwide and is associated with substantial costs to the individual and society, making it a significant global public health concern. We present evidence for trauma as a public health issue by highlighting the role of characteristics operating at multiple levels of influence - individual, relationship, community, and society - as explanatory factors in both the occurrence of trauma and its sequelae. Within the context of this multi-level framework, we highlight targets for prevention of trauma and its downstream consequences and provide examples of where public health approaches to prevention have met with success. Finally, we describe the essential role of public health policies in addressing trauma as a global public health issue, including key challenges for global mental health and next steps for developing and implementing a trauma-informed public health policy agenda. A public health framework is critical for understanding risk and protective factors for trauma and its aftermath operating at multiple levels of influence and generating opportunities for prevention.

From 2021 onwards, female entrepreneurship is expected to grow very substantially as a result of the Covid-19 pandemic. The introduction of teleworking and staggered hours in many countries at national or workplace level will make possible the conciliation between labour and family life. The purpose of this paper is to identify the most influential explanatory factors in the behaviour of female entrepreneurship in Europe so as to subsequently propose efficient economic policy measures to promote it. The distinction between opportunity and necessity female entrepreneurs have been considered since both motivation and factors are different in each case. 15 econometric models using the panel data method for a sample of 20 previously selected European countries (grouped by their GDP level) during the period 2001 to 2018 have been estimated to determine which explanatory factors affect female entrepreneurship and necessity-based female entrepreneurship. The empirical analysis used demonstrates that more women enter into entrepreneurship due to necessity rather than in pursuit of opportunity for European countries both with higher levels of GDP and for countries with lower levels of GDP. In this context, the following policy measures should be implemented to promote female entrepreneurship in Europe: the optimization of government spending (training courses and mentoring, public procurement, stronger networks, support in reconciling business and family life, etc.), the government incentives for subsidizing high interest rates to support women in accessing financing, and the improvement of entrepreneurship education to increase the confidence of women in themselves in their own entrepreneurial capabilities.

Since the launch of the Science Based Targets initiative (SBTi), we have witnessed a steady increase in the number of companies committing to climate targets for large-scale reduction of greenhouse gas (GHG) emissions. While recent studies present various methodologies for establishing climate targets (e.g., sectoral decarbonization approach, near-term, long-term, net zero), we still don’t understand the explanatory factors determining the level of ambition companies demonstrate in target setting. In this paper, a two-stage qualitative study is conducted with a sample of 22 companies from five countries. First, these companies’ publicly disclosed climate targets are evaluated according to four target ambition criteria: target type, scope, timeframe, and temperature alignment. Secondly, multiple explanatory factors for target setting were identified during the content analysis of the interviews to see how present these factors appear in the ambition levels. Within companies with highly ambitious climate targets, the findings indicate that certain factors are highly present, including leadership engagement, continual management support, employee involvement, participation in climate initiatives, and stakeholder collaboration. Conversely, none of these key factors are highly present in companies with less ambitious climate targets. Rather, these companies strongly identify the initiating factors of market-related pressures and non-market stakeholder influence as being the driving forces behind their target setting. This paper contributes to the literature on corporate responses to climate change by expanding our understanding of explanatory factors for different corporate climate target ambition levels.

The increasing rates of forest cover change and heightened vulnerability to deforestation present significant environmental challenges in Northeast India. This study investigates the dynamics of forest cover change and susceptibility to deforestation in this region from 2001 to 2021, utilizing data from the Hansen Global Forest Change (HGFC) product on the Google Earth Engine (GEE) platform. A suite of multicriteria decision-making (MCDM) models—including VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), Simple Additive Weighting (SAW), Evaluation Based on Distance from Average Solution (EDAS), and Weighted Aggregates Sum Product Assessment (WASPAS)—was employed to assess changes in forest cover and deforestation susceptibility across varied zones. Multicollinearity tests confirmed the relevance of the factors influencing deforestation. Statistical validations, such as the Wilcoxon Signed Ranks Test, underscored the models' robustness, revealing statistically significant outcomes. Additionally, Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) analysis demonstrated the superior fit of the VIKOR model (AUC = 0.938) compared to SAW (AUC = 0.901), EDAS (AUC = 0.895), and WASPAS (AUC = 0.864) in predicting current deforestation susceptibility. Validation affirmed the reliability of all MCDM methods, with VIKOR displaying high sensitivity (True Positive Rate, TPR = 0.878) and optimal AUC (0.938). Correlation analyses among the models identified significant inter-relationships, notably a positive correlation between EDAS and SAW, and a negative correlation between VIKOR and SAW. The regions of Assam, Nagaland, Mizoram, and Arunachal Pradesh were identified as experiencing significant forest cover loss, indicating a pronounced susceptibility to future deforestation. These findings underscore the need for immediate intervention to address this critical environmental issue. Graphical Abstract

Urban expansion is one of the most dramatic forms of land transformation in the world and it is one of the greatest challenges in achieving sustainable development in the 21st century. Previous studies analyzed urbanization patterns in areas with rapid urban expansion while urban areas with low to moderate expansion have been overlooked, especially in developed countries. In this study, we examined the spatiotemporal dynamics of urban expansion patterns in South Florida, United States (US) over the last 25 years (1992–2016) using Remote Sensing and GIS techniques. The main goal of this paper was to investigate the degree and spatiotemporal patterns of urban expansion at different administrative level in the study area and how spatiotemporal variance in different explanatory factors influence urban expansion in this region. More specifically, this research quantifies the rates, types, intensity, and landscape metrics of urban expansion in Miami-Fort Lauderdale-Palm Beach, Florida Metropolitan Statistical Area (Miami MSA) which is the 7th largest MSA and 4th largest urbanized area in the US using remote sensing (satellite imageries) data from National Land Cover Datasets (NLCD) and Coastal Change Analysis Program (C-CAP) at 30 m spatial resolution. We further investigated the urban growth patterns at the county and city areas that are located within this MSA to portray the local ‘picture’ of urban growth in this region. Urban expansion in this region can be divided into two time periods: pre-2001 and post-2001 where the former experienced rapid urban expansion and the later had comparatively slow urban expansion. Results suggest that infilling was the dominant type of urban expansion followed by edge-expansion and outlying. Results from landscape metrics represent that newly developed urban lands became more aggregated and simplified in form as the time progressed in the study region. Also, new urban lands were generated away from the east coast and historic cities which eventually created new urban cores. We also used correlation analysis and multiple linear stepwise regression to address major explanatory factors of spatiotemporal change in urban expansion during the study period. Although the influence of factors on urban expansion varied temporally, Population and Distance to Coast were the strongest variables followed by Distance to Roads and Median Income that influence overall urban expansion in the study area.

Effective and thorough credit-risk management is a key factor for lending institutions, as significant financial losses can arise from the borrowers’ default. Consequently, machine learning methods can measure and analyze credit risk objectively when at the same time they face increasingly attention. This study analyzes default payment data from a credit cards’ portfolio containing some 30,000 clients from Taiwan with twenty-three attributes and with no missing information. We compare prediction accuracy of seven classification methods used, i.e. KNN, Logistic Regression, Naïve Bayes, Decision Trees, Random Forest, SVC, and Linear SVC. The results indicate that only few out of most of the typical variables used can adequately analyze default characteristics in terms of lending decisions. The results provide effective feedback to credit evaluators, lending institutions and business analysts for in-depth analysis. Also, they mention to the importance of the precautionary borrowing techniques to be used to better understand credit-card borrowers’ behavior, along with specific accounting, historical and demographical characteristics.

The data collected from the survey in this study revealed that a total of 2431 firms successfully filled out and returned the questionnaire. Among these, 73.79% were categorized as non-adopters of CIFRS, whereas 26.21% were recognized as adopters of CIFRS. The findings derived from the logistic regression model indicated that the latent variables, including financial reporting components, stakeholder knowledge and attitudes, the internal control system, and the costs related to the implementation of CIFRS, exerted a highly positive and statistically significant impact on the probability of adopting CIFRS in Cambodia at the 1% significance level. However, the variable concerning financial reporting components demonstrated significance only at the 5% level. It is important to mention that the estimated latent variables were determined using explanatory factor analysis (EFA). The estimated coefficients for the control variables were 0.997 for number of employees, 1.243 for assets, 5.581 for filing financial reports with ACAR, -0.725 for ACAR's enforcement of laws related to CIFRS implementation, and -0.644 for inconsistencies in financial information provided by CIFRS compared to standards set by the General Department of Taxation. Each parameter associated with the control variables had statistical significance at the 1% level.

Background Prolonged emergency department length of stay (EDLOS) is a worldwide issue associated with increased mortality, decreased patient satisfaction and poor quality of care. The factors influencing EDLOS have not been comprehensively studied in the context of Swedish EDs. This study’s objective is to determine the input-, throughput- and output factors associated with EDLOS, at two urban EDs in Sweden. Methods Data was collected from two hospitals. All patient visits during the two-year study period were included. Patients who left without being seen by a physician were excluded. The explanatory factors included patient characteristics, medical data, and hospital bed occupancy data. Multi-variable linear regression analysis was used to test the associations between the factors and EDLOS. Results The top contributors to prolonged EDLOS were diagnostic imaging, which added between 64 and 149 min of EDLOS, diagnostic testing at central laboratory (53–99 min), followed by intra-ED zone transfer (46–94 min). Arriving during crowding or being admitted during high hospital bed occupancy had a significant but relatively small absolute effect on the outcome. Conclusions Throughput factors had far greater impact on EDLOS than both input- and output factors. Adapting strategies to the structural and procedural characteristics of each setting may enhance the effectiveness of improvement efforts. Clinical trial number Not applicable.

Mobility limitations, including a decrease in walking speed, are major issues for people with autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS). Improving our understanding of factors influencing walking speed in ARSACS may inform the development of future interventions for gait rehabilitation and contribute to better clinical practices. The objective of the study was to identify the factors influencing the self-selected walking speed in adults with ARSACS. The dependent variable of this cross-sectional study was the self-selected speed and the factors (independent variables) were age, sex, balance, balance confidence, knee flexion and extension cocontraction indexes, lower limb coordination, passive range of motion of ankle dorsiflexion, knee and hip extension, and global spasticity. Multiple regression models were used to assess the relationships between walking speed and each factor individually. Six factors were significantly associated with walking speed and thus included in regression models. The models explained between 42.4 and 66.5% of the total variance of the self-selected walking speed. The factors that most influence self-selected walking speed are balance and lower limb coordination. In order of importance, the other factors that also significantly influence self-selected walking speed are ankle dorsiflexion range of motion, lower limb spasticity, knee extension range of motion, and confidence in balance. Balance and lower limb coordination should be targeted in rehabilitation interventions to maintain walking ability and functional independence as long as possible. The six factors identified should also be included in future studies to deepen our understanding of walking speed.

Epidemiological estimates of substance use disorders (SUD) are critical for the planning of evidence-informed intervention and services. In this study, 250 incarcerated individuals in Nigeria were interviewed with the Mini International Neuropsychiatric Inventory (MINI) to diagnose SUD and antisocial personality disorder (ASPD). Most of the participants were males (97.6%), and the mean age was 35.4 (SD=13.5) years. Substance use disorder and ASPD were prevalent in 57.6% and 11.2% of the participants, respectively. Of those diagnosed with SUD, 35.2% and 22.4% had poly-SUD and mono-SUD respectively. Psychotic and dependence syndromes involving cannabis misuse were the most prevalent poly-SUD, and mono-SUD was characterized by alcohol, nicotine, and opioid dependence syndromes. Substance use disorder was more likely in participants charged with robbery and convicted, while ASPD was associated with prior and long-term imprisonment. There is a need for effective integration of treatment for ASPD/SUD into correctional mental health services in settings with inadequate health care using an appropriate model and a viable strategy.