Planning Staff Research Articles

Reliability optimization through soft digital twins

This paper presents a framework for developing a “soft digital twin” (SDT) for reliability optimization through modeling and simulation. The focus is on demonstrating the effectiveness of the SDT in modeling maintenance task processes within mission-critical facilities, which is particularly essential for reliable operations in critical infrastructure such as nuclear facilities. The need for efficient completion of maintenance tasks (MTs) to ensure high reliability and minimal downtime underscores the challenge faced by managers, who desire a predictive tool akin to a “crystal ball” for optimal resource configuration in the face of uncertainties from equipment failures, staffing issues, and supply chain disruptions. The proposed SDT functions as this predictive tool, leveraging the simulation’s versatility to provide insights into resource configurations and staff planning. This work extends the prior research by incorporating additional model validation, sensitivity testing, and analysis of the impact of various resource changes on the system. It employs a combination of data-driven frameworks and stochastic modeling methods to construct an adaptive SDT capable of accommodating changes in system behavior. The work provides a framework for the construction of SDT, testing and validation of its results, and its application to a case study of a mission-critical facility focused on replication and then optimizing of two key performance indicators based on human resource mixtures. Initial findings suggest that the SDT yields reliable results comparable to retrospective test datasets, offering the potential to minimize MTs’ time in the system while maximizing throughput within specified time frames through scenario experimentation and optimization.

Theory, practice and research: the Quebec nursing experience

In Canada, and particularly in Quebec, the development of a research culture in nursing is booming, both in general and advanced practice. In this illustrated study, the perspective of a hybrid administrative and humanistic conceptual model is used to understand a staffing plan for nurses and caregivers in a private seniors' residence in Quebec City. Based on this qualitative research, it is possible to examine the under-representation of the nurse-caregiver's role in these settings.

Ready to act? Factors associated with nurses’ desire to work in Primary Health Care in Italy

Abstract Background The rapid increase in demand for primary healthcare (PHC) services after the pandemic requires reconsidering nursing staff planning and availability. The objectives of the study were to i) identify knowledge, attitudes, practices and work expectations of future nurses in primary care; ii) estimate the prevalence of nursing students who desire to be employed in PHC and Family and Community Health Nursing (FCHN) services after graduation. Methods A cross-sectional study is being conducted on a sample of nursing students enrolled in universities in the Lazio Region. A structured instrument was built based on the literature and national regulations and validated (Cronbach’s alpha=0.81). Results In this first phase, 163 nursing students were enrolled. The majority of the sample was female (N = 125, 76.7%), with a mean age of 22 (SD ± 5) years and were enrolled in the last year of degree program (N = 85, 52.1%). A total of 49 students (30%) reported adequate knowledge of PHC service reorganization policies and nursing practice in this setting. While 74% (N = 122) of the sample intended to undertake a PHC curricular internship during their degree program, only seven students (4%) participated in one. 52 individuals (30%) expressed their intention to be employed in PHCs after graduation, and 29 (17.8%) indicated their desire to work as FCHN practitioners. The main factors contributing to the undesirability of employment in PHC reported by students were: i) preferring the hospital settings (N = 53, 48%); ii) not having adequate skills to be employed in the PHC (N = 21, 19%); and iii) believing that the PHC does not offer attractive services for nursing practice (N = 14, 13%). Conclusions A multivariable analysis will be conducted to identify predictors of nursing students’ desire to work in PHCs after graduation. Preliminary results of the study showed that only one-third of nursing students indicated a desire to work in PHCs. Key messages • Only one-third of nursing students express a desire to work in PHC and in Family and Community Health Nursing (FCHN) services post-graduation. • Collaboration between the PHC workforce, academics, stakeholders, and policymakers is needed to create tailored initiatives that increase the attractiveness of PHC.

Experiences and Lessons Learned From Surging the Governmental Public Health Workforce During the COVID-19 Pandemic.

During the COVID-19 pandemic, the United States relied on the public health workforce to control the spread of COVID-19 while continuing to provide foundational public health services. Facing longstanding staffing shortages, state, tribal, local, and territorial (STLT) public health agencies (PHAs) used various strategies and supports to surge the workforce in response to the pandemic. The study explored (1) the types of strategies and supports STLT PHAs used to surge the public health workforce during the pandemic, (2) assessments of surge strategies and supports, and (3) approaches to using the range of surge strategies available. We conducted 27 semi-structured interviews in 2023 and performed thematic analysis. Interviewees included STLT public health officials, leaders of organizations that directly supported the COVID-19 workforce surge, and public health workforce experts indirectly involved in the workforce surge. To surge the workforce, interviewees relied on partnerships, staffing agencies, the National Guard, the Centers for Disease Control and Prevention Foundation's COVID-19 Corps, the Medical Reserve Corps, and other strategies. Interviewees valued strategies that rapidly engaged staff and volunteers at no cost to their PHA and flexible funding from the federal government to support surge efforts but noted shortcomings in the strategies and supports available. Interviewees described using multiple strategies simultaneously but noted challenges in implementing these strategies due to inadequate planning and insufficient staff and resources. The study underscores the need for STLT PHAs to establish mechanisms to surge the workforce as part of ongoing planning for emergency preparedness. Focus areas include building administrative and hiring capacity within STLT PHAs and sustaining partnerships and contractual agreements that helped fill staffing gaps during the pandemic. To support efforts to build workforce capacity to meet future surge management needs, STLT PHAs should consider creative solutions to attract and retain staff, as well as opportunities to engage students in public health work.

Human Capital Turnover in the Construction Industry: A Review with Focus on Archies Construction Pvt. Ltd

This present research focuses on identifying the reasons behind the high rates of turnover among staff at Archies Construction Pvt Ltd and how the problem can be solved. Retirement, voluntary or forced termination is equally costly within the construction business as it heavily affects schedule, cost and organizational performance. This research also studies the internal factors referring to human capital theory, namely job satisfaction, reward, and promotion, management support, and the coercive factors including market trends and fluctuations, competition, and regulation. Employing survey and qualitative interviewing methods, the study combines the closed, quantitative survey data collected from employees and qualitative data obtained from the interviews with current and former employees of the company. The study brings out several internal factors that are important drivers of turnover such as lack of satisfaction with leaders, absence of career development in organizations, and poor employee remuneration. The problem is further compounded by external forces like the competition witnessed when seeking for the qualified personnel and the volatility of the market forces. The assessment points out that high turnover rates raise costs of hiring and training, impact project duration and employees’ morale thereby diminishing a firm’s stability. In response to these challenges, it is suggested that the following retention strategies should be adopted: it is proposed that leadership should communicate more effectively with employees; organizations should provide enhanced career progression plans for staff; and organizations should ensure that rewards and remuneration given to employees are competitive. Moreover, an enhancement of the degrees of employee engagement and supportiveness of workplace climate could enhance employee turnover. In other words, by managing both internal and external sources of turnover, it is possible for Archies Construction to maintain a stable workforce, exhibit sound labor productivity and, therefore, improve its performance on projects in general and its financial performance in specific. The study enhances the existing knowledge on HCM in industries that rely on projects and offers best practices for addressing turnover. Further research could focus on changes in economic or technological factors pertaining to workforce retention in the construction industry.

Predictor Selection for Case Complexity Based on Routine Data for Needs-Based and Competence-Based Staff Planning.

Due to nursing staff shortage and growing nursing care demand, resource allocation and optimal task distribution have become primary concerns of nursing management. Grade mix analysis based on nursing interventions and nurse qualifications from routine patient documentation can support this. Case complexity is a key linking factor of nursing interventions, workload, and grade mix. This study determined case complexity predictors based on one year of routine patient documentation (n = 3,373 cases) from a Swiss hospital and predicted the patient clinical complexity level via weighted cumulative logistic regression models. Significant predictors were sex, age, pre-admission residence, admission type, self- care index, pneumonia risk, and number of nursing interventions. The models' accuracy is limited yet appropriate for applications such as needs- and competence- based staff-planning. After calibration via in-hospital data it could support nursing management in these tasks. The next step is now to test the model in a clinical setting.

An exact algorithm for the multi-trip vehicle routing problem with time windows and multi-skilled manpower

Motivated by the challenges of non-emergency patient transportation services in the healthcare industry, this study investigated a multi-trip vehicle routing problem incorporating multi-skilled manpower with downgrading. We aimed to find an optimal plan for vehicle routing and multi-skilled manpower scheduling in tandem with the objective of minimizing the total cost, including travel and staff costs, without violating time windows and lunch break constraints. To address this, two mathematical models were formulated: an arc-flow model and a trip-based set-covering model. In addition, a branch-and-price-and-cut algorithm, based on the set-covering model, was proposed to solve practical-scale instances. To determine the feasibility of the integer solutions, we introduce a feasibility check model. To address the multi-trip characteristics of the proposed problem, a novel two-phase column generation algorithm was introduced to solve the subproblem. This approach differs from traditional one-phase labeling algorithms and involves a tailored labeling algorithm for obtaining non-dominated labels in the first phase and a strategy to identify the trip with the minimum reduced cost for each label in the second phase. Furthermore, novel and efficient staff-based inequalities were developed by improving the k-path inequalities. Extensive numerical experiments were conducted to demonstrate the solution performance of the proposed algorithm and reveal managerial insights for non-emergency ambulance operations. The results demonstrate that our algorithm can successfully solve instances with up to 50 patients to optimality within two hours. Moreover, we demonstrated the value of jointly optimizing vehicle routing and staff planning, which can result in significant cost savings of up to 19.4%.

Lean approach applied to the blood gas system in point-of-care testing (POCT): The IRCCS San Gerardo dei Tintori experience

ABSTRACT Costs of medical care have increased worldwide, which may be due to unnecessary operational inefficiency. Different approaches exist to enhance procedural processes, and the Lean concept is used to improve quality and performance by systematically eliminating waste. In this study, Lean methodology with the four-stage Deming cycle (Plan, Do, Check, and Act) was applied to the point-of-care testing (POCT) system of the IRCCS San Gerardo dei Tintori. Starting from the evaluation of the current strategies used in the clinical laboratory, our goal was to facilitate workflow standardization, increase the efficiency, appropriateness, and quality standards in the execution of blood gas analysis in POCT, and to plan improvements, staff satisfaction, and safety. Six qualitative key process indicators, such as workflow, work area management, visual checks, reagents and supplies inventory, sample traceability, and work standardization, were used to investigate problems and identify solutions. The adoption of cutting-edge technologies, the training of personnel on the best pre-analytical practices, the standardization of cleaning procedures, the distribution of easy check workflow charts, and standardized material request documents reduced manual operations from 70 to 40% and patient identification errors of 10x. This approach created a diffuse collaborative culture and improved standardization of processes and safety.

Role of Artificial Intelligence in Revenue Management and Pricing Strategies in Hotels

Purpose: The general objective of the study was to investigate the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of Artificial Intelligence in revenue management and pricing strategies in hotels. Preliminary empirical review revealed that the integration of artificial intelligence (AI) into revenue management and pricing strategies significantly enhanced the financial performance and operational efficiency of hotels. AI's ability to process large datasets in real-time improved demand forecasting and dynamic pricing, leading to increased revenue per available room (RevPAR) and average daily rate (ADR). Additionally, AI facilitated personalized guest experiences, boosting customer satisfaction and loyalty. Despite these benefits, the study identified challenges such as high implementation costs, data privacy concerns, and the need for robust data infrastructure. Addressing these issues through strategic planning and continuous staff training was deemed essential for maximizing AI's potential in the hotel industry. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations theory, Technology Acceptance Model (TAM) and Resource Based View (RBV) may be used to anchor future studies on the role of AI in revenue management and pricing strategies in hotels. The study concluded that integrating AI into hotel revenue management and pricing strategies significantly enhances performance, contributing to both theoretical and practical advancements. It enriched the Diffusion of Innovations Theory by demonstrating factors influencing AI adoption in hospitality. Practically, it provided actionable insights for hotel managers on leveraging AI for improved key performance indicators and balancing dynamic pricing with customer satisfaction. Policy recommendations included establishing guidelines for AI implementation, enhancing data infrastructure, fostering a culture of innovation, and addressing skills gaps through training and development programs. The study emphasized the need for robust data management systems and regulatory support to facilitate AI adoption.

Artificial Intelligence (AI) applications and usage among the LIS professionals of Pakistan

Artificial intelligence (AI) is an important and emerging sub-discipline in information technology that is progressively being implemented in every field. It is gradually being introduced to support new forms of research, discovery, and reuse of library contents in advanced and interesting ways. University libraries have the potential to substantially improve their library services through the implementation of sophisticated AI tools. This study explored the application of AI tools in the university libraries of Pakistan, as well as draw a comparison in the usage of AI tools between public and private sector universities. This is a quantitative study and data is collected through survey methodology. We used purposive sampling to collect the data from 175 university libraries. The collected data was analyzed using a statistical package for social sciences (SPSS-version 22). Findings indicate that while AI-based services are starting to be introduced into university libraries in Pakistan, no university library has implemented the full suite of AI-based tools. Natural language processing, voice searching, and chatbots are the most familiar and popular tools currently used in libraries. However, robotics technology is rarely used with a mean value of (1.62) because of the financial investment and high level of IT skills required. We found that private university libraries are using AI tools more as compared with public sector university libraries. The study concludes with several key recommendations, including closer collaboration between the library and the respective university IT department for technical support and assistance; improved financial support and ICT infrastructure to establish AI technology-based library services; and training development plans for library staff. Insights gained from this study should contribute to the capacity of Pakistani University librarians and their staff to maximize the full potential of AI within their institutions. The research implications are helpful to library leaders and policymakers in building a policy for AI-based technology in their respective university libraries.

Design of a mathematical model for staff planning in an emergency department

The emergency department plays a fundamental role in hospitals and critically affects a hospital's overall efficiency. Inadequate staff planning in emergency departments generates high costs, overcrowding, and patient dissatisfaction due to long waiting times, possibly putting the patient's health and life at risk. This research designed two mixed integer linear programming mathematical models. The first determined the optimal number of physicians required per shift and weekday in an adult emergency department to minimize the deviation between available and required capacity. The results of the optimization model would reduce by 16.07 % the required medical office staff per week, from 56 physicians in the current situation to 47, reducing staffing costs without impacting waiting times. Moreover, the overall physician utilization would be 95.01 % compared to 77.79 % in the current situation, indicating an adequate distribution of physicians on each shift of each day according to patient demand. These results contribute to the problem of high medical staff costs and overcrowding without sacrificing timeliness and quality of care. In contrast, the second model that minimized the number of physicians considering capacity constraints would increase the staff by 7.14 % concerning the current situation. This research was based on a model presented in the literature, but the objective function included the deviation variables as unrestricted in sign and a constraint to ensure that they were positive. The first model designed is presented as a tool to support emergency department managers in the medium-term planning of medical staff, ensuring an optimal solution to this problem

Exploring causes of ineffective material management and impact on successful project deliverables in civil engineering construction projects in Sri Lanka

Purpose Construction material management plays a significant role in achieving successful project delivery of a construction project. However, ineffective material management is a critical issue in the construction industry, especially in developing economies, of which Sri Lanka is not an exception. Therefore, this study aims to focus on exploring the causes of ineffective material management practices in civil engineering construction projects in Sri Lanka and their impact on successful project delivery. Design/methodology/approach Furthermore, the literature findings were validated through the preliminary survey. Subsequently, a quantitative research approach was adopted to pursue the research aim. Questionnaire responses were obtained from 215 construction professionals in civil engineering projects who were selected using the judgemental and snowball sampling techniques. Collected data were analysed through Statistical Package for the Social Sciences (SPSS) V26 and Microsoft Excel 2016. Findings Moreover, the study revealed that material price fluctuation, shortage of material in the market, delay in material procurement, inadequate planning and delays in material delivery are the most frequent causes of ineffective material management in civil engineering projects. In addition, it was evidenced that most ineffective material management practices cause both time and cost overruns in civil engineering construction projects. Most respondents emphasized inadequate planning, inadequate qualified and experienced staff, lack of supervision and lack of leadership as the causes for both time and cost overruns. Originality/value The study was concluded by proposing strategies for effective material management. Education/training/enlightenment of staff in charge of materials management, use of software like Microsoft Project, Primavera and similar software to eliminate manual errors in material management, and providing clear specifications to suppliers were the most agreed strategies for effective material management in civil engineering construction projects.

Strava Metro Data

The widespread adoption of mobile phone and other location-tracking devices, and the enormous amounts of data they produce, has provided municipalities with the opportunity to automate previously time-consuming and labour-intensive data collection processes. Municipal planners, in particular, have begun to integrate the aggregated data sets of private urban technology platforms into active transportation and broader infrastructure planning initiatives. To date, however, there has been limited research on the implications of this integration for municipal decision-making and governance processes. Using the Strava Metro data stream and its free-access model as a case study, this paper explores both the motivations behind municipal adoption of the Strava platform and the benefits that accrue from its usage. Through the application of a mixed methods approach, including the building of a use case database via a search of internet and academic literature sources and qualitative interviews with municipal planning staff, our research examines how Strava data is used to support the work of municipal planners and evaluates the strengths and weaknesses of that use. Our study finds that Strava Metro data aided municipal staff in the planning of cycling and pedestrian infrastructure, complementing available in-house data sets; helped spur new active transportation initiatives; and enabled innovation and professional curiosity on the part of planners. The paper concludes by exploring the ramifications of Strava data for community wellness and broader public realm improvements, as well as extending a discussion with respect to the platform’s sociodemographic representativeness and related limitations.

Planning for uncertain transportation futures: Metropolitan planning organizations, emerging technologies, and adaptive transport planning

In the U.S., Metropolitan Planning Organizations (MPOs) play a pivotal role in regional transportation planning. Emerging transportation technologies present new challenges for transportation planning practice, which is experiencing growing uncertainty not only from these new technologies and other uncertainties, and MPOs must increasingly plan for the needs of new technologies and innovation. This study investigates how MPOs are currently planning for emerging technologies by analyzing regional transportation plans (RTPs) of the 50 largest MPOs and interviewing planning staff from 17 MPOs. We examine the extent to which anticipatory governance and responsible innovation, which come from science and technology studies (STS), are integrated into MPO planning efforts, shedding light on trends in transportation planning theory and practice. Findings reveal limited integration of anticipatory governance and responsible innovation into their planning processes. Some include aspects of foresight and engagement, but reflexivity, flexibility, and responsiveness are much less developed. Key actions being taken include more comprehensive thinking about region-specific impacts of technologies, developing policies, piloting technologies, building partnerships, and creating new tools and planning models. The extent to which these practices are creating adaptive capacities within MPOs is still limited. To address this, we propose an adaptive transportation planning model that combines anticipatory governance and responsible innovation with long-range transport planning. This integration is crucial for aiding MPOs and other planning agencies in developing robust governance systems, methodologies, and public policies to effectively manage technology within urban environments and navigate the increasingly complex challenges posed by emerging technologies and other uncertainties.

Medical deserts in Spain-Insights from an international project.

Medical deserts are a growing phenomenon across many European countries. They are usually defined as (i) rural areas, (ii) underserved areas or (iii) by applying a measure of distance/time to a facility or a combination of the three characteristics. The objective was to define medical deserts in Spain as well as map their driving factors and approaches to mitigate them. A mixed methods approach was applied following the project "A Roadmap out of medical deserts into supportive health workforce initiatives and policies" work plan. It included the following elements: (i) a scoping literature review; (ii) a questionnaire survey; (iii) national stakeholders' workshop; (iv) a descriptive case study on medical deserts in Spain. Medical deserts in Spain exist in the form of mostly rural areas with limited access to health care. The main challenge in their identification and monitoring is local data availability. Diversity of both factors contributing to medical deserts and solutions applied to eliminate or mitigate them can be identified in Spain. They can be related to demand for or supply of health care services. More national data, analyses and/or initiatives seem to be focused on the health care supply dimension. Addressing medical deserts in Spain requires a comprehensive and multidimensional approach. Effective policies are needed to address both the medical staff education and planning system, working conditions, as well as more intersectoral approach to the population health management.

Specifying cross-system collaboration strategies for implementation: a multi-site qualitative study with child welfare and behavioral health organizations

BackgroundCross-system interventions that integrate health, behavioral health, and social services can improve client outcomes and expand community impact. Successful implementation of these interventions depends on the extent to which service partners can align frontline services and organizational operations. However, collaboration strategies linking multiple implementation contexts have received limited empirical attention. This study identifies, describes, and specifies multi-level collaboration strategies used during the implementation of Ohio Sobriety Treatment and Reducing Trauma (Ohio START), a cross-system intervention that integrates services across two systems (child welfare and evidence-based behavioral health services) for families that are affected by co-occurring child maltreatment and parental substance use disorders.MethodsIn phase 1, we used a multi-site qualitative design with 17 counties that implemented Ohio START. Qualitative data were gathered from 104 staff from child welfare agencies, behavioral health treatment organizations, and regional behavioral health boards involved in implementation via 48 small group interviews about collaborative approaches to implementation. To examine cross-system collaboration strategies, qualitative data were analyzed using an iterative template approach and content analysis. In phase 2, a 16-member expert panel met to validate and specify the cross-system collaboration strategies identified in the interviews. The panel was comprised of key child welfare and behavioral health partners and scholars.ResultsIn phase 1, we identified seven cross-system collaboration strategies used for implementation. Three strategies were used to staff the program: (1) contract for expertise, (2) provide joint supervision, and (3) co-locate staff. Two strategies were used to promote service access: (4) referral protocols and (5) expedited access agreements. Two strategies were used to align case plans: (6) shared decision-making meetings, and (7) sharing data. In phase 2, expert panelists specified operational details of the cross-system collaboration strategies, and explained the processes by which strategies were perceived to improve implementation and service system outcomes.ConclusionsWe identified a range of cross-system collaboration strategies that show promise for improving staffing, service access, and case planning. Leaders, supervisors, and frontline staff used these strategies during all phases of implementation. These findings lay the foundation for future experimental and quasi-experimental studies that test the effectiveness of cross-system collaboration strategies.

Design of a model for improving emergency room performance using a colored Petri net

Emergency rooms are one of the most complex and vital areas of healthcare institutions, which have presented overcrowding, long waiting, and length of stay times, affecting the timeliness, responsiveness, and quality of service. This research aimed to design a detailed patient flow model to improve emergency room performance using the hierarchical timed colored Petri nets. Then, the model was simulated to evaluate scenarios considering tactical decisions such as physician staff planning, operational decisions such as adjusting work schedules, and strategic decisions such as increasing observation beds. The best scenario would reduce the average waiting times for triage II patients by 17.30 % and 47.57 %, and triage III by 33.49 % and 43.49 % for medical consultation in the office or the minor surgery room, respectively. In addition, the waiting time in observation and the rate of patients left without being seen by a physician would be reduced by 92.45 % and 74.67 %, respectively. These results improve the quality and timeliness of the service and avoid putting the patient's health and life at risk. The designed model included more attributes for patients concerning the place of medical care in the emergency room, the number of visits to the physician, and the physician who will care for the patient. Moreover, the simulation model includes observation beds as a limited resource blocking new patient admission. Finally, this model is a tool to support emergency room managers in making short, medium, and long-term decisions to address problems such as overcrowding, long waiting and length of stay times, and high rates of patients left without being seen by a physician

A collaboratively produced model of service design for children and young people with common mental health problems

BackgroundLittle is known about the effectiveness of, and implementation complexities associated with, service delivery models for children and young people (CYP) experiencing ‘common’ mental health problems such as anxiety, depression, behavioural difficulties and self-harm. This paper outlines how a model for high-quality service design for this population group was developed by identifying available services, their effectiveness, cost-effectiveness and acceptability, and the barriers and enablers to access.MethodsSequential, mixed-methods design, combining evidence syntheses (scoping and integrative reviews of the international literature) with primary research (a collective case study in England and Wales). Data from these two elements were collaboratively synthesised in a subsequent model-building phase.ResultsThe scoping review yielded a service model typology. The integrative review found effectiveness evidence only for four models: collaborative care (the only service model to also have cost-effectiveness evidence), outreach approaches, brief intervention services and an organisational framework called ‘Availability, Responsiveness and Continuity’. No service model seemed more acceptable than others. Three case study themes were identified: pathways to support; service engagement; and learning and understanding. The model-building phase identified rapid access, learning self-care skills, individualised support, clear information, compassionate and competent staff and aftercare planning as core characteristics of high-quality services. These characteristics were underpinned by four organisational qualities: values that respect confidentiality; engagement and involvement; collaborative relationships; and a learning culture.ConclusionsA consistent organisational evidence-base for service design and delivery in CYP’s mental health spanning many years appears to have had little impact on service provision in England and Wales. Rather than impose – often inflexible and untested – specific local or national models or frameworks, those commissioning, designing and delivering mental health services for CYP should (re)focus on already known, fundamental components necessary for high-quality services. These fundamental components have been integrated into a collaboratively produced general model of service design for CYP with common mental health problems. While this general model is primarily focused on British service provision, it is broad enough to have utility for international audiences.

Inclusive and intersectoral: community health improvement planning opportunities to advance the social determinants of health and health equity

BackgroundCommunity health improvement plans (CHIPs) are strategic planning tools that help local communities identify and address their public health needs. Many local health departments have developed a CHIP, yet there is a lack of research on the extent to which these plans address root causes of health disparities such as the social determinants of health. This study aims to inventory the social determinants of health included in 13 CHIPs and examine facilitators and challenges faced by local health departments and partners when trying to include the social determinants of health.MethodsWe conducted a comparative plan evaluation by scoring 13 CHIPs on their inclusion of equity orientation, inclusive planning processes, and five social determinants of health: health care access and quality, the neighborhood and built environment, economic stability, social and community context, and education access and quality. To supplement the plan evaluation, we conducted 32 in-depth interviews with CHIP leaders and stakeholders to understand the factors contributing to the inclusion and exclusion of the social determinants of health in the planning process.ResultsCHIPs received an average score of 49/100 for the inclusion of the social determinants of health. Most plans addressed health care access and quality and the neighborhood and built environment, but they often did not address economic stability, the social and community context, and education access and quality. Regarding their overall equity orientation, CHIPs received an average score of 35/100, reflecting a relative lack of attention to equity and inclusive planning processes in the plans. Interviews revealed that challenges engaging partners, making clear connections between CHIPs and social determinants, and a lack of capacity or public and partner support often led to the exclusion of the social determinants of health. Recommendations to improve planning processes include improving data infrastructure, providing resources for dedicated planning staff and community engagement incentives, and centering equity throughout the planning process.ConclusionsAlthough local health departments can leverage CHIPs to improve population health and address health disparities, they face a range of challenges to including the social determinants of health in CHIPs. Additional resourcing and improved data are needed to facilitate broader inclusion of these determinants, and more work is needed to elevate equity throughout these planning processes.

Dampak Perubahan Kebijakan Izin Mendirikan Bangunan (IMB) Menjadi Persetujuan Bangunan Gedung (PBG) di Kabupaten Padang Pariaman

The aim of writing this article is to see the impact of changing the building construction permit (IMB) policy to building construction approval (PBG) in Padang Pariaman Regency. Law Number 23 of 2014 concerning Regional Government states that the management of buildings in the regions is part of the implementation of government affairs in the field of public works and spatial planning which is the authority of the Regency/City government. This article applies a qualitative method by applying a descriptive analysis system in presenting it. The data collection technique for this research is a library research library study sourced from various reading literature and previous research articles regarding policy implementation. There are still obstacles in granting Building Approvals in Padang Pariaman Regency, lack of public awareness of the importance of PBG, lack of public understanding of the procedures and requirements for PBG applications through the SIMBG system and limited professional technical planning staff.