Multistage Method Research Articles

Application of a Reservoir Classification Method Based on Core Data from Offshore Tight Reservoirs: A Case Study of the Liushagang Formation in the Beibu Basin

Several methods are currently used to test offshore tight reservoirs. However, the effectiveness of these applications varies among wells, and some exhibit unclear reservoir classifications. These issues lead to difficulties in decision-making during tests and result in higher testing costs. Therefore, to address this issue, this study used reservoirs in the Liushagang Formation of the Beibu Basin as the research object and employed core data to apply the multi-stage FZI method. This method computes the FZI and its cumulative probability, classifying the target reservoir into seven distinct types. According to the Winland R35 method, the target reservoir was classified into five distinct types. Seven characteristic parameters were selected based on the mercury injection experimental data. The K-means clustering method was then used to classify the target reservoirs into two types. The conclusions were that, in this formation, there is predominantly low to extra-low porosity and extra-low to ultra-low permeability. According to relationship models, logged porosity can be used to calculate effective permeability. Combining the multi-stage FZI method with the K-means clustering method for reservoir classification is recommended. This integrated approach facilitates a more comprehensive analysis of the characteristics of offshore low-permeability tight reservoirs at both macro and micro scales after classification. This research provides key insights for enhancing offshore well production.

A Multi-Scale and Multi-Stage Human Pose Recognition Method Based on Convolutional Neural Networks for Non-Wearable Ergonomic Evaluation

In the context of industrial robot maintenance and assembly, workers often suffer from work-related musculoskeletal disorders (WRMSDs). This paper proposes a multi-scale, multi-stage pose recognition method (MMARM-CNN) based on convolutional neural networks to provide ergonomic intervention. The method leverages computer vision technology to enable non-contact data acquisition, reducing the interference of physiological and psychological factors on assessment results. Built upon the baseline yolov8-pose framework, the method addresses complex maintenance environments, which are prone to occlusion, by introducing the Lightweight Shared Convolutional Detection Head-pose (LSCD-pose) module, Multi-Scale Channel Attention (MSCA) mechanism, and Efficient Multi-Scale Patch Convolution (EMSPC) module, enhancing the model’s feature extraction capabilities. The MMARM-CNN model was validated using the MS COCO 2017 dataset and robot assembly data collected under laboratory conditions. The experimental results show that the MMARM-CNN achieved an accuracy improvement, reaching 0.875 in the mAP@0.5 evaluation. Overall, this method demonstrates significant potential in advancing the automation and intelligence of ergonomic interventions.

Acoustic and soliton propagation using fully-discrete energy preserving partially implicit scheme in homogeneous and heterogeneous mediums

This study presents an energy preserving partially implicit scheme for the simulation of wave propagation in homogeneous and heterogeneous mediums. Despite its implicit nature, the developed scheme does not require any explicit numerical or analytical inversion of the coefficient matrix. Theoretical analysis and numerical experiments are performed to validate the energy preserving properties of the fully-discrete scheme. Convergence analysis is also performed to assess the rate of convergence of the developed scheme. The efficiency and accuracy of the developed scheme are validated by numerical solutions of wave propagation in layered heterogeneous mediums. Furthermore, simulations of soliton propagation following nonlinear sine-Gordon and Klein-Gordon equations in homogeneous and heterogeneous mediums are discussed. Numerical solutions are also compared with the results available in the literature. The present method accurately resolves the physical characteristics of the chosen problems, competing well with existing multi-stage time-integration methods. Moreover, it has significantly lower computational complexity than the four-stage, fourth-order Runge-Kutta-Nyström method.

Temporal asynchrony analysis for dynamic operation of hydraulic-thermal-electricity multiple energy networks based on holomorphic embedding method

Analyzing the operational states of multiple energy networks (MEN) in multi-energy systems is crucial for ensuring system stability. The dynamic operational characteristics of different energy flows pose challenges for computational analysis. Traditional steady-state methods are inadequate for addressing the dynamics of MEN, especially when dealing with temporal discrepancies between hydraulic and thermal flows in thermal networks (TN) and the heterogeneity between TN and electrical networks. Therefore, this paper proposes a novel holomorphic embedding method (HEM) based on multi-stage decomposition method. The developed HEM constructs a time coefficient matrix and utilize inner-outer loop recursion to handle the time lag between thermal flow and hydraulic flow in the TN. Additionally, we reconstruct a holomorphic matrix, integrating hydraulic flow to bridge thermal and electric power flows, thereby improving the operational heterogeneity among different networks. Real-case simulations show that when the Taylor expansion order in HEM is equal to 4, the proposed method achieves a mere 1% discrepancy from actual operational data, enhancing computational efficiency by 60% compared to the Newton-Raphson method. Moreover, in this real-case, the TN exhibits a maximum delay response time of 180s compared to electrical networks. Exploiting this delay time effectively increases renewable energy generation within multi-energy systems by 961.58kW per day.

Beyond Illumination: Stakeholder Perspectives on Urban Lighting Master Planning for Chiang Mai Old City, Thailand

Chiang Mai Old City, a cultural heritage site and major tourist destination in Thailand, has significant cultural capital but lacks a well-designed urban lighting system, limiting its nighttime development potential. This issue arises from the absence of an urban lighting master plan, a crucial tool for guiding the city’s growth. The challenge lies in reconciling the diverse perspectives of stakeholders to create a comprehensive lighting master plan that meets shared goals. This research proposes a system dynamics approach to analyze stakeholder complexity. A qualitative, multi-stage method was employed, through in-depth interviews and focus groups with 60 stakeholders from three groups: government, professionals, and end users, to prioritize critical factors. Causal Loop Diagrams (CLDs) were used to illustrate the interrelations among those factors, leading to alternative scenarios for the lighting master plan’s development. The findings demonstrate that safety and security is the top priority, followed by cultural and economic factors. Eleven reinforcing loops and two balancing loops are proposed through CLD visualization. This framework highlights the importance of a participatory process, advocating for a systematic and holistic approach where all stakeholders with diverse perspective collaborate side-by-side in the development of the urban lighting master plan for Chiang Mai Old City.

A multi-phase mission success evaluation approach for maritime autonomous surface ships considering equipment performance degradation and system composition changes

With the development of Maritime Autonomous Surface Ships (MASSs), the mission success assessment problem cannot be ignored because of potential security issues of MASSs. This paper addresses the issue of performance degradation at different stages, the problem of state dependence in multi-stage missions of MASSs, and the correlation problem between missions of stages. Based on the coupling of the reliability model of each single-stage mission system, a multi-stage mission success evaluation method for MASSs is proposed. By leveraging the ability of the conditional probability tables in Bayesian networks to express the complex relationships between the nodes, the dynamic Bayesian network model of stages is constructed based on the fault tree. Based on the Markov process, the problem of state dependence on shared equipment between stages is solved. Considering the complex relationship between multi-stage missions, the virtual node is introduced, and the multi-state Bayesian network is combined to realize the coupling of the reliability evaluation results of each single-stage mission. It is applied to the multi-stage mission success evaluation of the MASS to obtain the success probability of MASSs and the key equipment of each stage. The results show that evaluation results are more suitable for engineering practice.

Interpretable diagnosis of breast lesions in ultrasound imaging using deep multi-stage reasoning

Objective.Ultrasound is the primary screening test for breast cancer. However, providing an interpretable auxiliary diagnosis of breast lesions is a challenging task. This study aims to develop an interpretable auxiliary diagnostic method to enhance usability in human-machine collaborative diagnosis.Approach.To address this issue, this study proposes the deep multi-stage reasoning method (DMSRM), which provides individual and overall breast imaging-reporting and data system (BI-RADS) assessment categories for breast lesions. In the first stage of the DMSRM, the individual BI-RADS assessment network (IBRANet) is designed to capture lesion features from breast ultrasound images. IBRANet performs individual BI-RADS assessments of breast lesions using ultrasound images, focusing on specific features such as margin, contour, echogenicity, calcification, and vascularity. In the second stage, evidence reasoning (ER) is employed to achieve uncertain information fusion and reach an overall BI-RADS assessment of the breast lesions.Main results.To evaluate the performance of DMSRM at each stage, two test sets are utilized: the first for individual BI-RADS assessment, containing 4322 ultrasound images; the second for overall BI-RADS assessment, containing 175 sets of ultrasound image pairs. In the individual BI-RADS assessment of margin, contour, echogenicity, calcification, and vascularity, IBRANet achieves accuracies of 0.9491, 0.9466, 0.9293, 0.9234, and 0.9625, respectively. In the overall BI-RADS assessment of lesions, the ER achieves an accuracy of 0.8502. Compared to independent diagnosis, the human-machine collaborative diagnosis results of three radiologists show increases in positive predictive value by 0.0158, 0.0427, and 0.0401, in sensitivity by 0.0400, 0.0600 and 0.0434, and in area under the curve by 0.0344, 0.0468, and 0.0255.Significance.This study proposes a DMSRM that enhances the transparency of the diagnostic reasoning process. Results indicate that DMSRM exhibits robust BI-RADS assessment capabilities and provides an interpretable reasoning process that better suits clinical needs.

High color rendering index WLEDs enabled by multi-band tuning of InP quantum dots

Quantum dots (QDs) represent a significant class of fluorescent materials, offering the potential to reduce power consumption and enhance the color rendering index (CRI) of white light-emitting diode (WLED) devices. However, the presence of toxic elements such as Cd and Pb in traditional fluorescent QDs limits their widespread commercial application. Compared to the broad emission characteristics of environmentally friendly AgInS2 and CuInS2 QDs, the narrower linewidth of InP-based core-shell QDs is advantageous for developing WLED devices with a higher CRI. In this study, we employed a multistage heating method to synthesize a series of amino-phosphine-based InP/ZnSe core-shell QDs, which exhibited emission wavelengths ranging from 535 to 650 nm, narrow emission linewidths of 43-47 nm, and high photoluminescence quantum yields of 60%-80%. Subsequently, six different color QDs are used to fabricate a WLED device. The best WLEDs show not only bright warm light (correlated color temperature = 3323 K) with a maximum luminous efficacy of 74.1 lm W-1, but also excellent color quality (CRI Ra = 93, as well as R9 = 94.8, and R13 = 97.1). These results indicate remarkable progress in InP-based WLEDs for high-quality lighting applications.

Multi-stage planning of clean resources and energy storage assets with hybrid uncertainty modeling for low-carbon resilient distribution systems

Climate change drives the urgent need for low-carbon and resilient energy system transitions. However, current planning methods ignore the inherent conflicts between carbon emission reduction and resilience enhancement, failing to optimally balance asset allocation for both aspects. They also neglect the long-term dynamic and stochastic nature of transitions, exposing systems to hybrid uncertainties. This paper presents a multi-stage dynamic planning method for clean resources and energy storage assets in power distribution networks. First, to facilitate low-carbon and resilient transitions, adaptive, stage-wise planning decisions are optimally determined under various planning strategies to mitigate risks stemming from hybrid uncertainties. Second, to precisely quantify the impact of hybrid uncertainties on decision-making, a systematic hybrid-uncertainty modeling approach is designed to capture short- and long-term uncertainties arising from both exogenous and endogenous factors. Third, to alleviate computational complexity, a decomposition-based stochastic dynamic dual integer programming method with augmented cut generation is introduced, which decomposes the original problem into stage-wise subproblems coordinated through forward and backward steps. Numerical results show that the proposed method generates cost-effective planning schemes, yielding $60 K in savings and reducing carbon emissions by 3853 tons. The method also demonstrates strong scalability, with a 52% reduction in solution time for large-scale planning problems.

Investigating the relationship between internet addiction, domestic violence, and emotional divorce among married women in Tehran

BackgroundIdentifying the factors affecting emotional divorce due to the negative consequences of this social problem in women can play an important role in preventing its occurrence. Therefore, the present study aimed to investigate the relationship between internet addiction, domestic violence, and emotional divorce among married women in Tehran.MethodsA cross-sectional survey was conducted among married women in Tehran, Iran. Sampling was conducted first through a multi-stage cluster method and then continued using a random method. Tehran was divided into five parts: central, northern, southern, eastern, and western. The sample size was divided by 5, resulting in 80 participants selected from each part. In each part, two regions were selected, and in each region, 40 houses were randomly selected. Researchers visited the selected houses, and the questionnaires were filled out at home by the women. Tools such as Gottman’s Emotional Divorce Test, Young’s Internet Addiction Test, and the Domestic Violence against Women Survey were used to collect data. Descriptive and inferential statistics were used to analyze the data in SPSS (version 24) software.ResultsA total of 400 women participated in the study. 46% of all married women suffered emotional divorce. The analysis of these tests showed that are positive and significant relationships between emotional divorce and internet addiction (r = 0.626, p < 0.0001), emotional divorce and domestic violence (r = 0.632, p < 0.0001), and internet addiction and domestic violence (r = 0.455, p < 0.0001). It was also found that there are significant negative relationships between emotional divorce and both the level of education (r=-0.115, p = 0.021) and employment status (r=-0.512, p < 0.0001). Additionally, there are significant negative relationships between internet addiction and age (r=-0.285, p < 0.0001), duration of marriage (r=-0.237, p < 0.0001) and employment status (r=-0.694, p < 0.0001). Furthermore, it was revealed that there are significant negative relationship between domestic violence and age (r=-0.104, p = 0.037) and level of education (r=-0.156, p = 0.002).ConclusionAlmost half of the married women in this study experienced emotional divorce, internet addiction, and domestic violence. These factors have a direct relationship with emotional divorce. Future studies are needed to understand how internet addiction might be associated with separation, emotional divorce and family problems.

Multi-Stage Rolling Grid Expansion Planning for Distribution Networks Considering Conditional Value at Risk

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users’ electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users’ electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account.

Unconventional Fracture Networks Simulation and Shale Gas Production Prediction by Integration of Petrophysics, Geomechanics and Fracture Characterization

The proficient application of multistage fracturing methods enhances the status of the Duvernay shale formation as a highly esteemed shale reservoir on a global scale. Nevertheless, the challenge is in accurately characterizing unconventional fracture behavior and predicting shale productivity due to the complex distributions of natural fractures, pre-existing faults, and reservoir heterogeneity. The present study puts forth a Geo-Engineering approach to comprehensively investigate the Duvernay shale reservoir in the vicinity of Crooked Lake. To begin with, on the basis of the experimental results and well-logging interpretations, a high-quality petrophysical and geomechanical model is constructed. Subsequently, the establishment of an unconventional fracture model (UFM) takes into account the heterogeneity of the reservoir and the interactions between hydraulic fractures and pre-existing natural fractures/faults and is further validated by 18,040 microseismic events. Finally, the analysis of well productivity is conducted by numerical simulations, revealing that the agreement between the simulated and observed production magnitudes exceeds 89%. This paper will guide the efficient development of increasingly important unconventional shale resources.

Ultra-processed foods intake in relation to metabolic health status, serum brain-derived neurotrophic factor and adropin levels in adults

BackgroundIn recent years, there has been a lot of discussion over the impact of ultra-processed foods (UPFs) intake on overall health of subjects. However, the association between UPFs intake and metabolic unhealthy (MU) status is still in a state of ambiguity. The current study assessed the relationship between UPFs intake and MU status with regard to brain-derived neurotrophic factor (BDNF) and adropin levels.MethodsA sample of Iranian adults (aged 20–65 years) was selected to participate in this cross-sectional study using a multistage cluster random-sampling method. UPFs intake was assessed by a validated food frequency questionnaire and NOVA classification. Concentrations of metabolic parameters, BDNF and adropin were determined through fasting blood samples. MU status was assessed according to the criteria proposed by Wildman.ResultsThe overall prevalence of MU phenotype among study participants (n = 527) was 42.5%. Higher intake of UPFs was associated with elevated odds of MU status in multivariable-adjusted model (ORT3 vs. T1=1.88; 95%CI: 1.02–3.45). Moreover, a positive association was observed between UPFs intake and hypertriglyceridemia after controlling all confounders (ORT3 vs. T1=2.07; 95%CI: 1.15–3.73). However, each tertile increase in UPFs intake was not significantly associated with serum BDNF (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\beta\\:$$\\end{document}=0.15; 95%CI: -0.05, 0.34; P = 0.14) and adropin (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\beta\\:$$\\end{document}=-1.37; 95%CI: -6.16, 3.42; P = 0.58) levels in multivariable-adjusted linear regression models.ConclusionOur findings suggested that higher consumption of UPFs was related to increased likelihood of MU status among a sample of Iranian adults. Further longitudinal studies are needed to verify the directionality and generalizability of the results to all adult populations.

An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

Patient’s willingness to pay for improved community health insurance in Tanzania

Globally, achieving universal health coverage remains significant challenge. Health insurance coverage in low- and middle-income countries is still low with only a few African countries managed to reach 50% coverage. This study aimed to investigate the factors influencing patients’ willingness to pay (WTP) for medication and various versions of the improved Community Health Insurance Fund (iCHF) in Tanzania. A facility-based cross-sectional study was conducted in all hospitals, health centres, and eight randomly sampled dispensaries, sampling participant from the queue, one out of every three patient based on their order of entry into consultation room, and interviewed 1,748 patients in Kilombero and Same districts in Tanzania. We used multi-stage Contingent Valuation Methods exploring data collected during client exit interviews. We employed a random utility model and estimated WTP through an ordered logit model. The independent variables were; patient’s gender, age, marital status, education, employment status, Non-Communicable Disease (NCD) status, health insurance status, and the type of healthcare facility level. Our findings revealed that most patients exhibited a WTP of an amount equivalent to the current iCHF premiums and would also be willing to pay for an augmented iCHF premium inclusive of additional medication coverage. Upon adjusting for demographic characteristics, we observed that patients enrolled in an insurance program or benefiting from user fee waivers demonstrated a lower WTP for medication, while those with non-communicable diseases (NCDs) and seeking care in private facilities exhibited a higher WTP. Furthermore, patients with a secondary education level or above generally displayed higher WTP for premiums. Conversely, patients enrolled in private insurance and availing user fee waivers, along with those accessing care in public facilities, demonstrated a lowered WTP for iCHF premiums. These results highlight the need for targeted interventions to address systemic deficiencies and improve access to medicines. Our conclusions is that policies considering NCD status, education levels and income status are important when designing health insurance schemes for the informal sector in Tanzania, with the goal of increasing uptake of CHF.

Synthesis of nanosized phases with a garnet structure using supercritical СО2 fluid

A method was developed for the production of nanosized complex oxides and oxysulfides. The first stage uses supercritical СО2 fluid (SAS – supercritical antisolvent method). This approach makes it possible to obtain precursor-free complex oxides in a narrow nanoscale range. Nanosized rare-earth iron garnets with the general formula R3Fe5O12, where R is a rare earth element, were obtained and studied by physicochemical methods. The resulting samples have a size of less than 100 nm, exhibit ferromagnetic ordering, and can be used as soft magnetic materials. The multi-stage method for the preparation of complex oxysulfides was not previously demonstrated anywhere in the literature. The method involves three stages. At the first stage, a nanosized X-ray amorphous solid solution of the original salts is obtained using the SAS method. Then a nanosized X-ray amorphous component – the oxide phase – is obtained by annealing in a furnace. After this, the resulting oxide phase is mixed with the disulfide of a transition element (Nb, Mo), and high-temperature annealing is performed in an evacuated quartz ampoule. As a result, compact and nanosized phases of the composition Eu3Fe5-3/2xMox□1/2xO12-2xS2x and Eu3Fe5-3/2xNbx□1/2xO12-2xS2x, where x = 0.15, were obtained for the first time. The introduction of the sulfide component, namely NbS2, into the garnet structure increases its magnetic parameters by the factor of 1.5.

Does green finance promote the transformation and upgrading of manufacturing enterprises? Empirical research based on micro survey data in China

This paper derives six research hypotheses related to the transformation and upgrading of green finance and manufacturing enterprises based on theoretical and literature analysis, and uses multi-stage non-strict πPSsampling method for micro data collection. The empirical study finds that green finance significantly promotes the transformation and upgrading of manufacturing enterprises. The transmission mechanism test based on the mediating effect model finds that resource allocation innovation, technological innovation, product innovation, organizational innovation and market innovation all play a partial mediating role in the process of green finance promoting the transformation and upgrading of manufacturing enterprises.

Deep Learning-Driven Multi-Stage Product Visual Emotional Design Utilizing Web Semantics

With the rapid development of artificial intelligence, the generation of emotional expression images has become a key research field. This article introduces a novel multi-stage emotion generation model cascade method that utilizes CGAN, Pix2Pix, and CycleGAN to create images with enhanced emotional depth and visual quality. We have outlined our approach, which involves a continuous process from emotional initialization to texture refinement, and then to style transition. Our experiments on facial and automotive datasets show a significant improvement in image quality compared to traditional models, with an average increase of 40 percentage points in structural similarity (SSIM) and an average increase of 11.1 percentage points in peak signal-to-noise ratio (PSNR). The research findings emphasize the potential applications of our model in advertising, entertainment, and human-computer interaction, where visual effects of emotional resonance are crucial.

Development of the Cultural Adaptations Content Checklist: Measuring cultural adaptations to evidence-based psychological interventions for racial and ethnic minoritized youth.

Disparities in mental health need and service use among racial and ethnic minoritized (REM) youth remain a pervasive public health concern in the United States. Cultural adaptations (CAs) have been put forth as a way to increase the cultural and contextual relevance of evidence-based psychological interventions (EBIs) to improve treatment outcomes among REM youth. Currently, no measure of CAs to EBIs for REM youth in the United States exists in the literature. Using a multistage method, this study sought to develop such a tool, the Cultural Adaptations Content Checklist (CACC). In a previous foundational study conducted via a systematic literature review, authors identified a comprehensive list of existing CAs. In the first two stages, we used a modified Delphi method as a part of which expert practice and research stakeholders (N = 17) provided feedback on CA types, definitions, and groupings via survey methodology. In the third stage, we conducted cognitive interviews with a subset of participants (n = 10) to assess participant comprehension of the CACC. The final CACC was developed and consists of 36 CA types organized into eight groups. Implications for use of the CACC in research and practice are presented. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Violent encounters on the front line: Sequential explanatory mixed-methods investigation of physical violence factors in the prehospital setting

ObjectiveWorkplace violence (WPV) is an important issue in prehospital care, especially for emergency medical technicians ( EMTs) who are at increased risk of physical violence due to the nature of their work. This study aimed to shed light on the specific factors that contribute to the underlying causes of physical WPV in the prehospital context through direct experience and insight into the work of EMTs.MethodsSequential explanatory mixed methods were applied in five western provinces of Iran from 2022 to 2023. In total, 358 EMTs that met the criteria for the quantitative phase were selected using a multi-stage clustering method. In the quantitative phase, the researchers used a questionnaire on workplace violence in the healthcare sector. Based on the results of the quantitative phase, 21 technicians who had experienced physical violence in the past 12 months were invited for in-depth interviews in the qualitative phase.ResultsThe average age of the EMTs was 33.96 ± 6.86 years, with an average work experience of 10.57 ± 6.80 years. More than half (53.6%) of the staff worked 24-hour shifts. In addition, most EMTs were located in urban bases (50.3%), and 78 (21.8%) reported having experienced physical violence. No significant correlations were found between the demographic characteristics of the technicians and the frequency of physical violence, except base location in the last 6 months. The qualitative study also created one theme (the complexity of WPV in the prehospital setting), four categories, and ten subcategories.ConclusionThe study’s results emphasize the need for comprehensive WPV factors in the prehospital setting. These factors can lead to identifying and improving strategies such as organizational support, improving communication and collaboration between responders, and training in de-escalation techniques. In addition, it is crucial to address the root causes of WPV such as poverty and lack of education in the community to create a safer and more supportive environment for patients and staff.