Planning Process Research Articles

Step by Step: Utilizing Kotter’s Model to Design and Implement a Strategic Plan for Institutionalizing Interprofessional Education and Practice

Health professional schools are complex environments. Academic leaders of interprofessional education and practice (IPEP) have the unique challenge of navigating these intricacies on the individual and collective level to create a common vision that supports the sustainable implementation and assessment of quality interprofessional education (IPE). In order to move beyond a common approach to IPE that has been described as a “series of isolated events” that insufficiently address the Interprofessional Education Collaborative (IPEC) core competencies in their entirety, a strategic planning process can help institutions design and develop robust IPE experiences with intentionality.This article describes the steps taken in a formal strategic planning process to create and sustain a pan-university IPEP office to strengthen IPE and collaboration among emerging health professionals/schools-departments at a public flagship university in the southeastern U.S. Additionally, Kotter’s model for institutional change highlights strategies to gain buy-in, and lessons learned. This paper provides a guide to help grow, sustain, and invest in formal IPEP programs for other academic institutions.

‘It’s not making a decision, it’s prompting the discussions’: a qualitative study exploring stakeholders’ views on the acceptability and feasibility of advance research planning (CONSULT-ADVANCE)

BackgroundHealth and care research involving people who lack capacity to consent requires an alternative decision maker to decide whether they participate or not based on their ‘presumed will’. However, this is often unknown. Advance research planning (ARP) is a process for people who anticipate periods of impaired capacity to prospectively express their preferences about research participation and identify who they wish to be involved in future decisions. This may help to extend individuals’ autonomy by ensuring that proxy decisions are based on their actual wishes. This qualitative study aimed to explore stakeholders’ views about the acceptability and feasibility of ARP and identify barriers and facilitators to its implementation in the UK.MethodsWe conducted semi-structured interviews with 27 researchers, practitioners, and members of the public who had participated in a preceding survey. Interviews were conducted remotely between April and November 2023. Data were analysed thematically.ResultsParticipants were supportive of the concept of ARP, with differing amounts of support for the range of possible ARP activities depending on the context. Six main themes were identified: (1) Planting a seed – creating opportunities to initiate/engage with ARP; (2) A missing part of the puzzle – how preferences expressed through ARP could help inform decisions; (3) Finding the sweet spot – optimising the timing of ARP; (4) More than a piece of paper – finding the best mode for recording preferences; (5) Keeping the door open to future opportunities – minimising the risk of unintended consequences; and (6) Navigating with a compass – principles underpinning ARP to ensure safeguarding and help address inequalities. Participants also identified a number of implementation challenges, and proposed facilitative strategies that might overcome them which included embedding advance research planning in existing future planning processes and research-focused activities.ConclusionsThis study provides a routemap to implementing ARP in the UK to enable people anticipating impaired capacity to express their preferences about research, thus ensuring greater opportunities for inclusion of this under-served group, and addressing the decisional burden experienced by some family members acting as proxies. Development of interventions and guidance to support ARP is needed, with a focus on ensuring accessibility.

Disentangling pre-service history teachers’ pedagogical content knowledge

History education would benefit from insight into teachers' pedagogical content knowledge [PCK]. A thinking-out loud protocol was used to get insight into 12 pre-service history teachers’ PCK in Belgium and the Netherlands. A reconstructed lesson planning process was used to disentangle the individual constituent components of PCK. Not all PCK components developed simultaneously, participants demonstrated more knowledge of teaching strategies than of their students or the curriculum. Some participants were only partly capable of establishing relationships between certain PCK components, a situation that can be interpreted as the result of cognitive overload. The implications for teacher education are discussed.

Shared Care Planning for a pregnant patient with uveal melanoma treated with interventional radiotherapy (brachytherapy)

Introduction: Uveal melanoma (UM) is the most prevalent primary intraocular tumor in adulthood, with an important risk of metastasis, notably to the liver, rendering standard chemotherapy ineffective. Cases of UM onset during pregnancy are rare but can exhibit accelerated tumor growth. For these cases the best conservative treatment is interventional radiotherapy (IRT) also known as brachytherapy (BT). It is a form of radiotherapy that demonstrates efficacy comparable to the more common enucleation, while mitigating any risks of intrauterine toxicity. Objective: We reported the case of M., a 39-year-old pregnant patient, diagnosed with UM and undergoing brachytherapy treatment. Our aim is to make pertinent ethical considerations about: a. The ethical and clinical approach favouring a treatment strategy that preserves the affected organ and its functionality, b. The advance care planning process conducted within a multidisciplinary team. Case discussion: With the objective of preserving the mother’s health and ensuring the protection of the foetus, the team unanimously agreed, through a collaborative decision-making process, to proceed with the viable brachytherapy intervention. This approach results in a lower burden and maximizes benefits for both the patient and the unborn child. Conclusions: Considering a holistic evaluation, especially considering the patient’s pregnancy, the multidisciplinary approach proves fundamental. Collaboration between bioethicists and health-care professionals facilitated the development of a shared document for healthcare ethics planning, expressing the team’s ethical-clinical considerations.

Landscape understanding and values in extended public consultations on spatial planning in rural communes in Poland

A large part of spatial planning projects in Polish rural communes deals with landscape issues such as protection and planning. Besides legal regulations extended public consultations (EPCs) on spatial planning processes provide an opportunity to comprehensively address and negotiate landscape arguments. This research aims to identify the understanding and value of the concept of landscape by local communities in the examined EPC cases. This paper explores landscape issues through a systematic analysis (multiple-case study) of six cases of EPCs carried out in Poland between 2019 and 2022. The landscape was negotiated in terms of its various functions to provide value for aesthetics, culture, history, identity, recreation, and economy. In some cases, the landscape was used as an argument to promote a hidden agenda. In the rural communes, aesthetic functions of landscapes were frequently addressed, while natural, ecological, and climatic functions of the landscape were barely mentioned, suggesting that the understanding of landscape remains at a representative, cultural, and visual level, which differs from urban discourses that often focus on climatic and ecological functions of landscape. EPCs are a sound instrument to further integrate landscape as a common asset into spatial planning processes.

Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN)

ABSTRACT Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.

Deep learning-based voxel sampling for particle therapy treatment planning

Objective. Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency. Approach. Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based on P-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels. Main results. On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection. Significance. The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.

Navigating the future of megaprojects sustainability: a comprehensive framework and research agendas

PurposeMegaprojects can play a crucial role in achieving Sustainable Development Goals (SDGs) and tackling Global Challenges. However, they are often criticized for their massive resource consumption, poor cost/time performance and significant social-environmental impacts, including irreversible environmental damage. This study aims to chart the evolution of research on megaprojects sustainability and to offer a roadmap for future developments.Design/methodology/approachThis study adopted a systematic literature review (SLR) to identify emerging themes and gaps. This study follows a three-step process of planning, conducting and reporting, based on the Tranfield approach. The overview of megaprojects sustainability research was captured through bibliometric analysis. In addition, content analysis was carried out to reveal the development of this field and get an insight into the future directions towards sustainability transition.FindingsThis study provides a longitudinal, in-depth analysis of megaprojects sustainability studies. Drawing on sustainability science and project management theories, we introduce a three-dimensional analytical framework consisting of sustainability, scope and stakeholder. This framework explains the evolution of megaprojects sustainability research from sustainability of megaprojects, to sustainability for megaprojects and then to sustainability by megaprojects. Three future avenues are proposed: (1) SDGs orientation at multi-level; (2) scope enlargement at temporal and spatial scales and (3) inclusive development for stakeholders.Originality/valueThis research contributes to the literature by providing a comprehensive and forward-looking analysis of megaprojects sustainability research. The framework and three research agendas provide a comprehensive picture of megaproject sustainability research; the agenda for future research is intended to inspire more studies and disruptive actions towards sustainability transition.

Reconsidering National Adaptation Plans (NAPs) as a Policy Framework under the UNFCCC

ABSTRACT Under the UNFCCC, the National Adaptation Plan (NAP) plays a central role as a policy framework for adaptation. While significant efforts, including research, have been made on the NAP scheme (which includes NAP formulation as one component), a critical evaluation of the entire NAP scheme is lacking. This includes an analysis of the relationship between the NAP process and the NAPs, their nature and intended functions, the interlinkages and implications of various COP agreements and guidelines on the NAPs, and alignment with the concept of the adaptation planning process. This paper analyzes COP Decisions, relevant documents from constituted bodies, and efforts by relevant international organizations to better understand how the NAP scheme is constructed and operated in order to clarify its strengths and weaknesses. To get an idea of how countries understand the COP Decisions and other relevant guidance for formulating their NAPs, we also reviewed the NAPs of nine countries in the Asia-Pacific region (Bangladesh, Cambodia, Fiji, Kiribati, Nepal, PNG, Sri Lanka, Timor-Leste, and Tonga). As a result, we find critical issues in the international agreements and actions on the NAPs that may become obstacles to global, and eventually national, adaptation efforts and make specific policy recommendations to address these challenges. Key policy insights Neither the NAP process nor the NAPs themselves have been well defined by the COP. As a result, formulated NAPs have no more in common than a ‘plan on adaptation developed by the national government.’ It is vital for the COP to define the NAP process and the NAPs more clearly, including their roles and relations, and to ensure consistent support for developing countries’ adaptation efforts through the NAP scheme. The COP should define the NAP process as the adaptation planning process (AP process) and find the roles of the NAPs as the ‘grand design of the AP process’ to enhance the consistency and effectiveness of global efforts on adaptation.

Confronting the effects of climate change through development plans

In recent years, climate change as a consequence of global warming, has garnered significant attention. Therefore, serious measures must be considered in the development planning process to adapt to and reduce effects of climate change. This research evaluates six development plans in Isfahan, Iran in the face of climate change effects using a framework consisting of three components of the analytical Awareness-Analysis-Action (A-A-A) approach and using quantitative and qualitative content analysis methods. In fact, the evaluation of the level of attention of the development plans from the perspective of the level of reflection and attention to raising awareness about the issues of climate change, the ability to analyze the available climate information in a useful way to support planning decisions and the level of special actions to deal with these changes. Finally, out of the three elements of the approach, the highest amount of attention in the plans is related to the element of action with 67/70% and the lowest is related to the element of awareness with 39/10%. The overall evaluation of the development plans shows that their role in integrating climate change issues into urban planning in Iran is still limited and insufficient. On the other hand, air pollution and climate change are closely related, as chemicals that cause air quality deterioration often enter together with greenhouse gases. Therefore, it can be said that although indicators of the transportation dimension (A3-J) with 80.66%, resource management have been given special attention under the action component, but carbon sequestration dimension (A2-F) with 14.63%, under the analysis component that seeks to evaluate the amount of air pollutants, classification of the type and sources of emission of pollutants could not reflect more than half of the total percentage in all development plans.

Antibiotic Use at the End of Life: Current Practice and Ways to Optimize.

Infections are common complications in end of life (EOL). However, clinicians have minimal guidance regarding antibiotic decision-making in EOL care, leading to the overuse of antibiotics. While symptom relief is frequently cited as a major reason for antibiotic use in EOL, antibiotics have not been shown to provide significant improvement in symptoms outside of urinary tract infections. In addition, when prognosis is expected to be in the range of days to weeks, antibiotics have not been shown to provide significant survival benefit. Antibiotics can be beneficial in EOL care in appropriate scenarios, but the current widespread use of antibiotics in EOL requires reevaluation. There needs to be broader efforts to think about antibiotics like other invasive medical procedures in which benefits and risks are weighed, recognizing that not all patients in EOL who receive antibiotics will benefit. In addition, during care planning process, discussing and documenting antibiotic preferences will be beneficial. Non-antibiotic symptom management should be incorporated to plan of care when infection is suspected. Ultimately, the use of antibiotics at EOL should be for the clear benefit for the recipient and should be guided by the type of infection and its clinical course, patients' primary disease and its prognosis, and the preferences of patients or surrogate decision makers.

Impact of an instructional team's feedback on an instructor's teaching practices in a Biology of Cancer course.

The effective implementation of evidence-based teaching (EBT) in large college courses benefits from the successful use of instructional teams. An instructional team's feedback allows instructors to act based on evidence of student learning, addressing students' needs. This feedback may be particularly important for novice instructors or experienced instructors teaching a class for the first time. This study sought to characterize the nature of an instructional team's feedback as well as its influence on the decisions and actions of a seasoned instructor teaching a new class. Instructional team members provided feedback in the form of anticipations, noticings, and suggestions. Anticipations and suggestions seemed to have the largest impact on the instructor's decisions and actions, while noticings, despite providing insights into student thinking, had a smaller effect. Our findings indicate that an instructional team can provide valuable feedback to instructors when team members have an opportunity to meaningfully participate in the planning and teaching processes.

Reflections on the challenges of conducting an international multicentre randomized controlled trial of balance training in addition to pulmonary rehabilitation and its impact on fall incidence in people with COPD

BackgroundPulmonary rehabilitation (PR) is accepted as standard care for individuals with COPD. We conducted an international, multi-centred randomized controlled trial (RCT) to determine if adding balance training to PR would reduce the incidence of falls in people with COPD. While there have been many trials investigating the effectiveness of PR, few have involved international collaboration. Successful execution of rehabilitation trials requires a significant investment of time, staffing, and resources. With the recent completion of the Balance Training for Fall Reduction in COPD RCT, we report on the design, implementation, and execution of our trial using project management phases. We also highlight our lessons learned for consideration in future multi-centre rehabilitation trials.MethodsThis was a retrospective review of the planning, preparation, timelines, and personnel training involved in the execution of this study using four of the five project management phases described by Farrell et al. in 2010: (1) initiation, (2) planning, (3) execution, and (4) monitoring and controlling. We report descriptive statistics as percentages and counts and summarize our lessons learned.ResultsTen outpatient PR programs in three continents participated. Thirty-one personnel worked on the trial across all sites. Enrolment began in January 2017 and was suspended in March 2020 due to the COVID-19 pandemic. Approximately 1275 patients were screened, 455 (36%) were eligible, 258 (57%) consented, 243 (53%) participated, and 130 (61%) completed the 12-month follow-up assessment. Lessons learned through our experience included (1) ensuring awareness of funder policies and considering the impact on collaborating sites; (2) preparing for the possibility of human resource and program disruptions; (3) anticipating site dropout and having a contingency plan in place; (4) planning and monitoring process measure data before, during, and after trial initiation; (5) ensuring frequent and consistent communication with and between collaborating sites; (6) maximizing features of database platform to ensure data set completeness and controlled data access; and (7) identifying strategies for increasing patient engagement in a high-demand study.ConclusionsWe identify seven lessons learned through our experience conducting an international, multicentre rehabilitation-based RCT. These lessons can provide guidance to other trialists conducting studies with similar logistics and may assist with future trial planning and implementation.

A novel 3D LiDAR deep learning approach for uncrewed vehicle odometry

Self-localization and pose registration are required for sound operation of next generation autonomous vehicles under uncertain environments. Thus, precise localization and mapping are crucial tasks in odometry, planning and other downstream processing. In order to reduce information loss in preprocessing, we propose leveraging LiDAR-based localization and mapping (LOAM) with point cloud-based deep learning instead of convolutional neural network (CNN) based methods that require cylindrical projection. The normal distribution transform (NDT) algorithm is then used to refine the former coarse pose estimation from the deep learning model. The results demonstrate that the proposed method is comparable in performance to recent benchmark studies. We also explore the possibility of using Product Quantization to improve NDT internal neighborhood searching by using high-level features as fingerprints.

An automatic robot for ultrasonic partial discharge detection of gas-insulated switchgear

Purpose Gas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks. Design/methodology/approach GBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device. Findings The robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate. Practical implications The robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields. Originality/value The robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

MT-SIPP: An Efficient Collision-Free Multi-Chain Robot Path Planning Algorithm

Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP.

Developing an efficient planning process for heritage buildings maintenance in Iraq

Abstract Heritage buildings and sites are significant assets for any country, they hold values of the past to the new generation. This research focuses on the problem of deterioration and demolition of heritage buildings due to negligence or aspiration for more profitable projects, which will lead to the loss of valuable heritage buildings. Assuming that this problem can be solved initially from the planning stage, this research aims to enhance the planning process for heritage buildings by specifying the main factors that affect the planning process for the maintenance work and developing a framework of sequential steps for planning process activities to help in creating an efficient plan. The research evaluates the current situation of heritage building conservation in Baghdad, Iraq as a case study, by classifying the relevant categories into maintenance and non-maintenance categories, which describe the reasons that led to the deterioration of numerous local heritage buildings. The results found in this research specify the main factors that lead to an efficient plan that are budget, methods and techniques, level of skills of the workers, schedule, and required materials. A framework for a successive conservation plan is suggested to be adapted, including a series of sequenced processes to get a sound plan. Finally, some recommendations are suggested to develop planning for heritage building conservation, like reevaluating the legislations and laws to enhance the heritage sector, serious work to make the heritage sites take place in the community activities, paying maximum care to the heritage sector by the governmental authorities, and using modern tools in planning in addition to other recommendations.

Investigating the impact of urban growth on land use using spatial autocorrelation methods in Konya/Türkiye.

Land use land cover (LULC) change, global environmental change, and sustainable change are frequently discussed topics in research at the moment. It is important to determine the historical LULC change process for effective environmental planning and the most appropriate use of land resources. This study analysed the spatial autocorrelation of the land use structure in Konya between 1990 and 2018. For this, Global and Local Moran's I indices based on land use data from 122 neighbourhoods and hot spot analysis (Getis-Ord Gi*) methods were applied to measure the spatial correlation of changes and to determine statistically significant hot and cold spatial clusters. According to the research results, the growth of urban areas has largely destroyed the most productive agricultural lands in the region. This change showed high spatial clustering both on an area and a proportional basis in the northern and southern parts of the city. On the other hand, the growth in the industrial area suppressed the pasture areas the most in the north-eastern region of the city, and this region showed high spatial clustering on both spatial and proportional scales.

Mapping human‐carnivore coexistence: approaches to integrating anthropogenic influences on carnivore distribution and connectivity modelling

AbstractInterdisciplinary approaches to modelling human‐wildlife coexistence have gained much attention in recent years, especially regarding carnivores which are subject to historical human persecution and dynamic attitudes and cultural values. Accordingly, there have been many attempts to gain insights into anthropogenic influences on carnivores through the use of species distribution models and connectivity analyses. However, there have been no global reviews on how anthropogenic influences are incorporated into such models. We conducted a literature review of global terrestrial carnivore species distribution and connectivity studies from 1995 to 2021 to evaluate the type of measure, frequency of use and spatial scale of the predictor variables used to describe environmental conditions and assess anthropogenic influences in such predictive models. We evaluated 2495 variables from 263 carnivore distribution and connectivity studies and found that variables used to describe environmental conditions are four times more commonly used than those used to assess anthropogenic influences on carnivores. However, the number of anthropogenic covariates used in carnivore distribution and connectivity studies has increased by 4.7% per year, and the spatial resolution of those covariates has simultaneously decreased by 7.6% per year. We observed that anthropogenic covariates were used more in studies occurring in the global north and for larger‐bodied carnivores (&gt;15 kg), suggesting the need for better integration of anthropogenic metrics into predictive models more widely and for smaller‐bodied carnivores. To help guide and advance the further use of sociological data into predictive spatial models for carnivores, we propose a framework that emphasizes integration of sociological methodologies and data sources ranging from exploratory expert elicitation to targeted focus group interviews at multiple phases in the conservation planning process.

A Maturity Model to Determine the Degree of Utilization of Machine Learning in Production Planning and Control Processes

The presented work introduces a maturity model for evaluating Machine Learning implementations, with a primary focus on Production Planning and Control processes, as well as broader organizational and technical aspects in companies. This model emerges as a response to the research gap identified in the analysis of 14 existing maturity models, which served as foundational bases for the development of this novel approach. By examining success factors and obstacles at different maturity levels, categorized according to defined dimensions and overarching design fields, this model can serve as a catalyst for bridging the research gap between models demanded in practice and the scholary exploration of topics related to Machine Learning in corporate processes. Notably, the structured design of this maturity model ensures accessibility for small and medium sized enterprises (SMEs).