Four-step Approach Research Articles

Implications of digital risks on teachers' motivation and intention to use digital tools: a PLS-POS perspective in Romanian preuniversity education system

PurposeThe study aims to analyze the different types of risks related to the use of technology and determine their positive or negative influence on teachers' motivation and behavioral intention to use digital tools.Design/methodology/approachThe research is based on survey data from 200 teachers in the Romanian preuniversity education system. The data analysis followed a four-step approach, using a partial least squares structural equation modeling (PLS-SEM) model for hypothesized relationships among research concepts and a PLS prediction-oriented segmentation (POS) procedure.FindingsThis study showed that increased risk awareness influences both motivation and, consequently, the intention to adopt digital tools in the preuniversity education system.Research limitations/implicationsThe scope of research remains constrained with regard to the examined population, considering the substantial number of teachers within the preuniversity education system. Another limit lies in the basic classification of identified risk types.Practical implicationsSchool managers should design a strategy to increase the level of motivation for integrating digital tools in the educational process.Originality/valueLittle scholarly attention has been devoted to investigating the risks associated with digitalization in the preuniversity education system. In addition, no prior research has been conducted to assess the influence of risk perception on people's motivation and intention to use digital tools in preuniversity education.

A data-driven traffic shockwave speed detection approach based on vehicle trajectories data

Traffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves’ turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow.

Analyzing the factors affecting construction project schedules amidst COVID-19 pandemic

Analyzing the factors affecting construction project schedules amidst COVID-19 pandemic

Sustainability assessment of faecal sludge treatment technologies for resource recovery in Phnom Penh, Cambodia

Sustainability assessment of faecal sludge treatment technologies for resource recovery in Phnom Penh, Cambodia

Why and how to implement worked examples in upper division theoretical physics

Studying worked examples has been shown by extensive research to be an effective method for learning to solve well-structured problems in physics and mathematics. The effectiveness of learning with worked examples has been demonstrated and documented in many research projects. In this work, we propose a new four-step approach for teaching with worked examples that includes writing explanations and finding and correcting errors. This teaching method can even be implemented in courses in which homework performance constitutes part of the grading system. This four-step approach is illustrated in the context of Lagrangian mechanics, which is ideal for the application of worked examples due to its universal approach to solve problems.

Skill retention efficacy of conventional versus modified Peyton's four-step intravenous access skill training approach

Abstract Background: Limited research has explored the impact of skill laboratory (lab) training on skill retention. Aims: This study aimed to assess and compare the effectiveness of skill retention between the modified Peyton’s four-step (MPFS) skill lab training approach and the conventional approach (CA) for intravenous (I.V.) access. This research employed a randomized controlled study design conducted within the Skill Development Unit of a Private Medical College and Hospital. Materials and Methods: Fifty-eight first-year medical students were randomly allocated into two groups. These groups received the MPFS or CA approach in small-sized skill lab training sessions focusing on I.V. access. The teacher-to-student ratio was maintained at 1:8, and the duration of each skill session was meticulously documented. Student performance was video recorded right after the teaching session and again at 3 months. The evaluation of skill effectiveness on both occasions was conducted by an impartial faculty member using a binary checklist. Additionally, feedback from both students and faculty members was collected. Group characteristics were analyzed using the chi-square test, and the outcomes of the groups were compared with the Student t test. A P < 0.05 was deemed to be of statistical significance. Results: The MPFS group consistently achieved significantly higher scores and had more proficient students during both assessment times (12 and 13) compared to the CA group, which had lower scores at both time points (3 and 4). Notably, there was no decline in skill proficiency within either group at the 3-month mark. The CA group expressed their appreciation for the chance to demonstrate independent performance, while the MPFS group placed value on receiving feedback and undergoing repeated observations. It is worth noting that both training approaches were manageable for faculty; however, the MPFS approach necessitated more time compared to the CA approach. Conclusion: The MPFS approach outperformed the CA method in acquiring I.V. access skills immediately after training and at the 3-month follow-up.

Effectiveness of endotracheal intubation and mask ventilation procedural skills training on second-year student using modified Peyton’s Four-Step approach during COVID-19 pandemic

ABSTRACT Background Airway emergency is the reason behind competency in mask ventilation and intubation skills for doctors. Procedural skills are taught through face-to-face training. However, the COVID-19 pandemic has had an impact on the education system, including medical education. The face-to-face training method cannot be carried out and requires a modification to an online method. Therefore, Peyton’s Four-Step Approach is modified to adapt this change. Objective Assessing the effectiveness of learning methods between modified Peyton’s Four-Step approach and classic Peyton’s Four Step approach in learning basic skills of mask ventilation and intubation during the COVID-19 pandemic. Method This was an experimental study with two groups of subjects in the Basic ClinicalSkills module of endotracheal intubation and mask ventilation at the Faculty ofMedicine, Universitas Indonesia during February-June 2022. The first group received training with classic Peyton’s Four-Step approach and another group with modified Peyton’s Four-Step approach. Both groups were tested and assessed using rubric score and global rating score, and asked to fill a feedback questionnaire. Results This study was conducted with 133 students in the classic group and 96 students in the modified group. The median rubric score was 21.2 for both groups and the global rating score showed 82%and 78% students passed, for classic and modified group respectively. Therubric score and global rating score, also the pass rate between two learningmethods showed no significant results (P > 0.05). The satisfaction and self-confidence questionnaires got answers ‘agree and strongly agree’ for all questions. Conclusion The learning method using modified and classic Peyton Four-StepApproach were equally effective for learning basic skill of endotracheal intubation and mask ventilation for students of the Faculty of Medicine,University of Indonesia. Both methods provided equal students’ satisfaction and self-confidence.

Operational strategies to deal with the COVID-19 emergency: recommendations from the Italian national society SIAGASCOT following the introduction of vaccines against the SARS-CoV-2 infection

BackgroundThis article aims to present the operational recommendations adopted by the Italian national society for orthopaedic surgery, arthroscopy, and sports medicine (SIAGASCOT) in managing patients eligible to undergo elective orthopaedic surgery during the COVID-19 pandemic after the beginning of a national vaccination campaign.Materials and methodsAn extensive literature search, analysing medical databases and scientific societies protocols, was performed to support this document. A four-step approach was used: 1—definition of priorities; 2—definition of significant clusters of interventions; 3—extraction of recommendations from international literature; and 4—adaptation of the recommendations to the specific features of the Italian healthcare system.ResultsThree operational priorities were defined (“continuity of care and containment of the virus spread”, “examination of waiting lists”, and “definition of the role of vaccines”), six significant clusters of intervention were identified, and recommendations regarding the risk management for healthcare staff and hospital facility as well as the preoperative, in-hospital, and postoperative management were produced. Patient selection, preoperative screening, and pre-hospitalization procedures, which are regarded as pivotal roles in the safe management of patients eligible to undergo elective orthopaedic surgery, were analysed extensively.ConclusionsThis document presents national-wide recommendations for managing patients eligible to undergo elective orthopaedic surgery with the beginning of the vaccination campaign. This paper could be the basis for similar documents adapted to the local healthcare systems in other countries.Level of evidenceLevel IV.

Community Stakeholders’ Perceptions of the Impact of the Coronavirus Pandemic on Homelessness in Canada

Homelessness was already a well-known risk factor contributing to premature death, morbidity, mental illness, and substance use disorder. The coronavirus disease for 2019 (COVID-19) pandemic has amplified disparities in Canada’s public health system, disproportionately impacting people experiencing homelessness. The present study aimed to investigate the impact of the COVID-19 pandemic on the homelessness situation in Canada from community stakeholders’ perceptions. The study used qualitative research approaches underlain by focused ethnography tenets. The sample includes 200 service providers from 28 communities across Canada (at least one site in each province and territory) to participate in virtual focus groups. Data analysis followed a four-step ethnographic approach for thematic analysis in qualitative research. Six main themes emerged: (a) system changes precipitated by the COVID-19 pandemic; (b) personal changes in life circumstances; (c) previous strategies no longer working; (e) opportunities; (d) some things getting better; (f) an overall increase in first time and recurrent homelessness in Canada. The study findings underscored mechanisms required to help ‘tip the scale’ in affording people experiencing homelessness the opportunity to avoid or exit homelessness.

A pre-seismic anomaly detection approach based on graph attention isomorphism network

Pre-seismic anomaly detection plays a crucial role in reducing economic losses and casualties caused by earthquakes. This paper proposes a novel four-step approach for pre-seismic anomaly detection. In the first step, a series of pre-seismic features are extracted by analyzing the earthquake catalog and geomagnetic signals. In the second step, the multi-view learning strategy is employed to obtain fusion features. In the third step, multiple seismic stations in one seismic zone are treated as a seismic station network, and a pre-seismic anomaly detection model is constructed based on the station network. In the final step, four evaluation indicators are introduced to comprehensively evaluate the detection results. Verification results show that the proposed method is effective and achieves better performance than other existing methods.

Implementing an ecosystem oceanography program to increase capacity and preparedness for dynamic ocean management and fishery challenges

Building resilience in coastal resources and related communities is improved by a holistic ecosystem research approach for integrating socioecological system components; a key challenge in this process is capturing dynamic interactions between components. We present an application of ecosystem oceanography to address goals of marine conservation and management, including climate readiness and supporting a blue economy. Treating the ecosystem as the sample unit is fundamental to our research program. Specifically, spatiotemporal structure of relationships among taxa themselves is the study subject, not the individual members. Our approach outlines four steps toward successful implementation: 1) Build a conceptual ecosystem-oceanography model informed by previous science and human dimensions research to test hypotheses and identify gaps in our understanding; 2) Design survey and adaptive monitoring efforts, including data sharing protocols, to capture the spatiotemporal processes of ecosystem structure; 3) Use diet data and spatiotemporal variability in trophic interactions to quantify processes influencing ecosystem function, including persistent hotspots of abundance, biodiversity, and trophic transfer; 4) Link empirically-determined processes to improve parameterization of biophysical models to enable evaluation of ecosystem structure and functionality retrospectively and prospectively. Accomplishing these objectives requires a transdisciplinary team and will enable evaluation of specific management goals, develop indicators for tracking progress towards meeting them, and carry out scenario evaluation under near-term and long-term scenarios that explore key uncertainties (e.g., future climate and policy directions). We apply this four-step approach to identify key drivers for recent ecosystem and fishery surprises in the California Current Ecosystem. We propose this approach offers a means for anticipating future ecosystem states and increasing preparedness and capacity to overcome fishery surprises, and in doing so supporting the development of management approaches that are robust to uncertainty.

Associations of psychoactive substances and steroid hormones in hair: Findings relevant to stress research from a large cohort of young adults

ObjectiveEpidemiological studies increasingly use hair samples to assess people’s cumulative exposure to steroid hormones, but how the use of different psychoactive substances may affect steroid hormone levels in hair is, so far, largely unknown. The current study addresses this gap by establishing the substance exposure correlates of cortisol, cortisone, and testosterone in hair, while also accounting for a number of relevant covariates. MethodData came from a large urban community-sample of young adults with a high prevalence of substance use (N = 1002, mean age=20.6 years, 50.2% female), who provided 3 cm of hair samples. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) quantified cortisol, cortisone, and testosterone, as well as delta-9-tetrahydrocannabinol (THC), 3,4-methylenedioxymethamphetamine (MDMA, “Ecstasy”), cocaine, several opioids, and their respective metabolites. Multiple linear regression models with covariates were used to predict steroid hormone levels from substance exposure in a four-step approach: In the full sample, low and high substance hair concentrations (median split) were first tested against no use for each substance individually (step 1) and for all substances together (step 2). Then, within the participants with any substance in hair only, the continuous hair concentration of each substance in pg/mg (step 3) and finally of all substances together, were regressed (step 4). ResultsLow, high, and continuous levels of THC in hair were robustly associated with higher levels of cortisol (sig. in step 1 low THC: β = 0.29, p = .021; high THC: β = 0.42, p = .001; step 2: low THC: β = 0.27, p = 0.036, and high THC: β = 0.40, p = .004, and step 4: β = 0.12, p = .041). Participants with high MDMA levels had higher levels of cortisone without adjusting for other substances (step 1: β = 0.34, p = .026), but this effect was not significant in the other models. While high THC levels were associated with lower levels of testosterone in step 2 (β = −0.35, p = .018), MDMA concentration was positively related to testosterone concentration with and without adjusting for other substances (step 3: β = 0.24, p = .041; step 4: β = 0.17, 95%, p = .015) in male participants. ConclusionThe use of psychoactive substances, especially of cannabis and ecstasy, should be considered in studies investigating steroid hormones in hair.

Monitoring the Sustainability of Building Renovation Projects—A Tailored Key Performance Indicator Repository

This study aims to assist in the identification of suitable key performance indicators (KPIs) that can be used to assess the sustainability performance of buildings given their transition into zero-carbon, resource-efficient, and resilient structures. To that end, a four-step methodology is proposed in this work; the first step includes the development of a KPI repository, which builds upon commonly accepted targets derived from the needs of different stakeholders as well as targets imposed by external factors. The second step refers to the expansion of the initial KPI repository, capitalizing on information from the literature. The third step includes the refinement of the repository based on predefined criteria (relevance, availability, measurability, reliability, and familiarity) and tailored feedback from key stakeholders. The final step concerns the development of KPI cards, which include all the necessary information for understanding and estimating the KPIs included in the final repository. This four-step methodological approach implementation was tested during the EU-funded HORIZON project ‘InCUBE’. The implementation of the first two steps of the proposed methodology resulted in a pool of 68 KPIs. Nearly half of these KPIs were extracted from Step 1 to fully support the monitoring of all InCUBE outcomes, while the rest of the KPIs were extracted from highly relevant Horizon frameworks, the B4P partnership, Level(S) framework, publications, and ETSI standards (Step 2). The implementation of Step 3 resulted in a shortlisted KPI pool which eventually defined the final InCUBE KPI repository, including 31 KPIs. To help with the coordination of the data gathering process and a shared understanding of the sort of information to be monitored among various stakeholders, selected KPI cards (Step 4) are presented.

Modeling of shared mobility services - An approach in between aggregate four-step and disaggregate agent-based approaches for strategic transport planning

Shared mobility services are slowly penetrating European cities. Hence, transport models that are capable of modeling them are fundamental, to support policy-makers for making informed decisions. Many European cities, especially the small- and the medium-sized ones, continue to use the traditional four-step modeling approach. This approach does not have the necessary capacity to model the shared mobility services and there is a need to extend them. Hence, this research proposes an extended framework, which includes modules for synthetic population generation and fleet management. Furthermore, modules are also suggested for the estimation of emissions, car-ownership and induced demand, as such measures are increasingly expected by cities. Multiple equilibrium checks between the aforementioned modules are avoided in the design of this development, to reduce model complexity and convergence issues. The models and the algorithms used for the additional modules in this study can be replaced with alternative equivalent models, provided the inputs and outputs are consistent with the framework. As such, the framework is software agnostic. Based on a case study on the city of Thessaloniki, a proof of concept is provided. The intermediate modeling framework, proposed in this study, provides an opportunity for cities to evaluate and integrate shared mobility systems and form long term planning strategies. Besides, it acts as a bridge between the worlds of simple and complex modeling approaches and pave the way for reducing the reservations of the cities toward complex approaches, while preparing them for a smoother transition in future.

Business Model Innovation in Manufacturing Firms: Exploring the Driving Forces of Digital Transformation

The adoption of digital technology has become a critical factor in the context of innovation management. The process of digital transformation leads to the realization of concrete business improvements that promote the innovation of entire business models. So far, it has remained unexplored what drives this business model innovation, especially when looking at digitally savvy firms from the manufacturing industry. This study aims to identify the key drivers of digital transformation of business models based on a systematic literature review. A four-step review approach draws on the findings from a core sample of 51 articles from two databases and results in the description of four key drivers for digital transformation of business models, namely ubiquitous connectivity, functional dematerialization, relational decentralization, and flexible scalability. Finally, a conceptual framework of four digital business model archetypes (lighthouse supplier, service enthusiast, data scientist, and platform owner) is proposed that considers a manufacturing firm’s business model exposure to the identified digital transformation drivers. While this study is limited to theoretical findings, it likewise provokes valuable follow-up thoughts for empirical academic work and practitioners from the manufacturing industries.

Estimating Path Travel Costs in Large-Scale Networks Using Machine-Learning Techniques

The integration of activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) is proposed as a prominent alternative to the traditional four-step approach to modeling transportation networks. In DTA, the least-generalized cost path-finding algorithm is executed to route travelers to their optimal paths, while ABM requires travel-cost skims of the activities, regardless of actual paths, to assess various options available to users such as destination and mode choices. These skims can be calculated either by rerunning the shortest-path-finding algorithm or storing the dynamic travel-cost skims generated at the DTA level. Having a large-scale network with multiple user classes, these methods require high memory storage and run time. Therefore, this study presents a heuristic algorithm utilizing machine-learning methods to predict origin–destination (OD) travel distances, times, and costs using historical vehicle trajectory data generated at the DTA level. This study uses an ellipse boundary around the ODs to truncate the network and extract the relevant vehicle sub-trajectories. Extracted sub-trajectories are clustered into with-toll and non-toll groups based on trip costs to estimate travel distance, time, and monetary cost accordingly. This approach can also be used in navigation systems to have a better estimate of arrival times. Numerical results for the greater Chicago network show promising performance in travel-cost measures estimation with improved solution times. To test the scalability of the proposed method for different OD pairs, the prediction of the best ellipse size is carried out using random forest and neural network methods.

Value of Using a Structured Improvement Approach: A Dedicated Education Unit Exemplar.

Using a structured approach to improvement that integrates methodological frameworks and commonly used improvement tools and techniques allows an improvement team to systematically implement and evaluate an intervention. Authors from one academic medical center will describe our four-step structured improvement approach in the design of a dedicated education unit (DEU) pilot to potentially meet a critical post-COVID-19 workforce need. Using our structured improvement approach, we successfully piloted a DEU with a unit-based clinical educator and 16 nursing students over 26 clinical days and received 13 preintervention surveys from participants. Although the DEU pilot was small with limited data, the structured improvement approach resulted in an organized and systematic way of designing, implementing, and evaluating improvement.

The "Four-step Approach" for Delivery of the Undergraduate Endodontics Experimental Course

Students' practical skills must be cultivated during undergraduate dental teaching and traditional theoretical examinations do not necessarily evaluate the relevant understanding and the application of knowledge. The current study aimed to produce a template with the potential for wider application for the improvement of practical dental teaching by applying a four-step approach to evaluating progress on an Endodontics course. Practical and theoretical examinations after one year's study via the four-step approach indicated significantly improved practical scores and no difference in theoretical scores compared with students taught by traditional methods. The suitability of the four-step approach, which incorporates continuous assessment and puts emphasis on practical skills, for dental education is discussed.

Mindfulness in paediatric occupational therapy practice: a phenomenological inquiry

Purpose While research into mindfulness practices is on the rise across populations, there is evidence to suggest that clinical practice has outpaced the literature with regard to mindfulness in pediatric rehabilitation. The aim of this study was to explore the perceptions of occupational therapists who opt to incorporate mindfulness into their clinical practices with children and youth. Methods Hermeneutic phenomenology was the methodology of the study. The theoretical framework employed a Heideggerian-informed phenomenology of practice. Eight occupational therapists practicing in Canada and the United States participated in 90–120 min semi-structured interviews that elicited first-hand accounts of mindfulness in pediatric occupational therapy practice. Interviews were transcribed verbatim and analyzed using Finlay’s four-step approach. Results Six salient themes were identified in the data: drawing from personal practice, enhancing participation, fostering healthy habits, adapting for children, keeping it playful, and doing with. Conclusion The findings of this study offer insights for therapists who are considering incorporating mindfulness into their practices with children and youth. Further, this research highlights a number of research priorities that require further inquiry.

Learning Green Social Work in Global Disaster Contexts: A Case Study Approach

Green social work (GSW) is a nascent framework within the social work field that provides insights regarding social workers’ engagement in disaster settings. Although this framework has recently garnered more attention, it remains under-researched and underdeveloped within the context of social work research, education, and practice in Canada and internationally. To further develop GSW in social work education and professional training, we considered how social work students and practitioners can use a learning framework to understand the impact and build their capacities to serve vulnerable and marginalized populations in diverse disaster settings. To do this, we developed a four-step case study approach, as follows: (1) provide detailed background information on the cases, (2) describe how each case is relevant to social work, (3) discuss how each case informs social work practice from a GSW perspective, and (4) provide recommendations for social work practitioners and students using GSW in future disaster-specific efforts. This case study approach centers on natural, technological, and intentional/willful hazards that examine current GSW research–practice engagement in Canada and internationally. Applying this four-step case study approach to three extreme events in Canada and internationally (a natural hazard, a technological hazard, and an intentional/willful hazard) illustrates it as a potential method for social work students and professionals to build their GSW capacities. This will assist in building the resilience of Canadian and international communities—especially those who have been historically marginalized. This article sheds light on how current social work education and professional training should develop new approaches to incorporate the GSW framework into the social work curriculum at large in order to prepare for future extreme events while incorporating environmental and social justice into research and practice.