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Background: The COVID-19 pandemic has affected education at all levels in various ways. This paper provides a review of the literature on the challenges of online learning for children with special educational needs and disabilities (SEND). Method: In total, 17 studies from nine countries were analysed. The challenges of online learning for children with SEND reported by teachers and parents and the strategies applied to overcome the challenges were identified. Results: The challenges of online learning were the need of parental support, routine change, inequities of resources and access to technology, lack of accommodations, and social isolation. There was a considerable lack of interventions aimed at promoting the educational outcomes of children with SEND. Conclusions: The findings emphasise the importance of parent-teacher collaboration and communication. Suggestions for strategies to address challenges as well as for further research are also discussed.

The prevalence and attributable risk of disease due to physical inactivity require it to be made a public health priority. Public health planning allows for prioritization and resource allocation, particularly at the state and local levels. The extent to which state planning efforts for physical activity exist in the United States is unknown. The purpose of this paper is to describe the scope in which physical activity is incorporated in state-level public health plans in the United States, with an emphasis on alignment with the national guidelines and the National Physical Activity Plan. A standardized internet search audit was developed and conducted for each of 50 US states and the District of Columbia between May 2017 and January 2018 to determine the prevalence and characteristics of health planning documents that include physical activity. Data abstracted for analysis used a standardized search protocol that included the components of the Physical Activity Guidelines for Americans and the US National Physical Activity Plan. Results found that most states had between 4 and 6 chronic disease prevention and control plans that mention physical activity; however, it was inconsistently aligned with recommendations from both the Guidelines and the National Plan. Only 2 states had stand-alone public health planning documents explicitly dedicated to physical activity promotion. No state planning documents addressed children and adolescents, adults, and older adults simultaneously. To be maximally effective, state public health planning for physical activity should be made a priority and these efforts should align as much as possible with current guidance from the Physical Activity Guidelines for Americans and the US National Physical Activity Plan. [ABSTRACT FROM AUTHOR]

Research problem: High turnover rates in long-term nursing facilities exacerbate the existing shortage of caregivers, a trend that will only worsen as the population of the United States ages. Part-time certified nursing assistants (CNAs) provide a significant amount of patient care in these facilities. CNAs also have high annual rates of turnover, which can harm health outcomes and increase the cost of care. In this study, we empirically analyze the effect of scheduling decisions on part-time CNA turnover. We explore three research questions that examine both how much and with whom a CNA is scheduled to work. We seek to empirically answer how (1) hours worked and (2) coworker variability influence turnover for part-time CNAs and if (3) coworker variability moderates the relationship between hours worked and CNA turnover. Methodology/results: Using novel data from one of the nation's largest nursing home organizations, which includes data for 6,221 part-time CNAs at 157 facilities in the United States over a 26-month period, we identify two scheduling levers managers may be able to use to influence turnover. As hours worked increase, turnover first decreases and then increases, demonstrating a nonlinear U-shaped relationship between hours worked and turnover. We also find that high coworker variability increases turnover while also moderating the effects of hours worked on turnover. In post hoc analyses, we demonstrate that high CNA turnover has negative impacts on patient health. Managerial implications: These findings suggest that managers may be able to leverage part-time CNA scheduling to reduce turnover, improving both the quality and cost of care. Specifically, we demonstrate that managers can reduce CNA turnover by increasing hours worked, scheduling coworkers together consistently and doing both simultaneously. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0533. [ABSTRACT FROM AUTHOR]

Wills and powers of attorney are two components of effective estate planning. Financial professionals are in a privileged position to identify roadblocks that prevent clients from establishing these critical components of an estate plan. When a client or loved ones are ill, incapacitated, or pass, these documents help guide individuals and families through tough times. This column provides a brief overview of the current landscape of wills and powers of attorney in the United States and how financial professionals can assist clients and their loved ones. [ABSTRACT FROM AUTHOR]

Scheduling Advertising on Cable Television Advertisement scheduling is a daily essential operational process in the television business. Efficient distribution of viewers among advertisers allows the television network to satisfy contracts and increase ad sale revenues. Ad scheduling is a challenging multiperiod, mixed-integer programming problem in which the network must create schedules to meet advertisers' campaign goals and maximize ad revenues. Each campaign must meet a specific target group of viewers and a unique set of constraints. Moreover, the number of viewers is uncertain. To solve this problem, S. Souyris, S. Seshadri, and S. Subramanian develop and implement a practical approach that combines mathematical programming and machine learning to create daily schedules. According to standard business metrics and the small integer programming gap, these schedules are of high quality. Using their methods, leading networks in the United States and India experience a 3% to 5% revenue increase, which translates to about $60 million annually for one prominent user. Advertisement scheduling is a daily essential operational process in the television business. Efficient distribution of viewers among advertisers allows the television network to satisfy contracts and increase ad sale revenues. Ad scheduling is a challenging multiperiod, mixed-integer programming problem in which the network must create schedules to meet advertisers' campaign goals and maximize ad revenues. Each campaign must meet a specific target group of viewers and a unique set of constraints. Moreover, the number of viewers is uncertain. To solve this problem, we develop and implement a practical approach that combines mathematical programming and machine learning to create daily schedules. These schedules are of high quality according to standard business metrics and the small integer programming gap. Leading networks in the United States and India using our methods experience a 3%–5% revenue increase. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2430. [ABSTRACT FROM AUTHOR]

Snowfall in the central United States has been shown to be highly variable at interannual timescales and subjected to long‐term linear trends. However, limited understanding is available about the intra‐annual variations and trends in snowfall within this region. Using a selection of GHCN daily snowfall observations and statistical analyses, this study develops a monthly climatology of snowfall characteristics in the central United States from 1948 to 2021. Here we find larger magnitude snowfall events have a greater relative frequency to seasonal snowfall late in the snow season, have increased in frequency in the western and northern regions of the basin, and contribute to a bimodal distribution of monthly snowfall in these sub‐regions. While snowfall has significantly increased over time within the northern and western regions of the domain by as much as 83%, snowfall has significantly decreased by much as 46% in the southern and eastern regions. Further, March snowfall totals and event frequencies have declined across the vast majority of the study domain, with maximum decreases in eastern IA of approximately 50%. When coupled with increases in snowfall and snowfall events in February, this suggests a shift in the snowfall season with time in this portion of the study region. Such results may be of particular interest to snow and water resource managers where integration of the nonstationarity of snowfall events and totals with time into management plans may yield better solutions. [ABSTRACT FROM AUTHOR]

Pediatric immunization is important for preventing potentially life-threatening diseases in children. Over time, the number of recommended pediatric vaccines has increased and is likely to increase further as new vaccines are developed. Given the different number of doses for available vaccines and various constraints (e.g., the appropriate age for each dose of a vaccine or the time between doses), it is challenging to develop a recommended vaccination schedule or a catch-up schedule when a child falls behind on one or more vaccinations. We developed an integer programming optimization model, enabled by Python programming and embedded into an Excel-based decision tool, to recommend childhood vaccination schedules or personalized catch-up schedules. The model recommends a vaccination schedule that balances the goal of being as close as possible to the clinically-indicated dosing schedules and the goal of minimizing clinic visits, and gives users the ability to trade off between these two goals. We illustrated the broad applicability of our proposed model with commonly-faced vaccine scheduling challenges in the United States. The illustrative computational case study confirms our model's ability to create personalized schedules based on each child's age and vaccination history, and to adjust appropriately when a new vaccine becomes available. The model presented in this paper fills the need for an easy-to-use tool to recommend vaccination schedules for de novo and catch-up purposes. It provides straightforward recommendations that can be easily used by physicians, is flexible to handle the requirements varying by region, and can be updated as new vaccines are approved for use. [ABSTRACT FROM AUTHOR]

Background: Infusion therapy refers to the intravenous administration of medicines and fluids for the treatment of status migrainosus, severe persistent headaches, or chronic headache. Headache practices and centers offer this treatment for patients as an alternative to the emergency department (ED) setting. However, little information is available in the literature on understanding the operations of an infusion center. Objective: We sought to survey the Inpatient Headache & Emergency Medicine specialty section and the Academic Program Directors listserv of the American Headache Society (AHS) to better understand current practices. Methods: A survey was advertised and distributed to the listservs of both the Inpatient Headache & Emergency Medicine specialty section and the Academic Program Directors, which combined included both academic and private practices. In addition, the survey was available on laptops at related events at an annual AHS meeting in Scottsdale. Results: Of the 127 members of the combined group of both listservs, 50 responded with an overall survey response rate of 39%. Ten out of fifty were from programs with more than one responder completing the survey, leaving 40 unique headache programs. Academic programs made up the majority of programs (85%, 34/40). The total of 40 participating programs is comparable with the 47 academic headache programs listed on the American Migraine Foundation website at the time of the survey. Of the academic programs surveyed, most were hospital based (n = 23) compared with a satellite location (n = 11). Of all programs surveyed, 68% (27/40) offered infusion therapy. Of those that did not have an infusion practice (n = 13), the most common reason cited was insufficient staffing (n = 8). Key highlights of the survey included the following: The majority of programs offering infusions obtain prior authorization before scheduling (70%, 19/27) and offer patient availability 5 days/week (78%, 21/27) typically only during business hours (81%, 22/27). Programs reported that they typically give three to four medications during each infusion session (72%, 18/25). Treatment paradigms varied between programs. Programs surveyed were concentrated in the Northeast and Midwest regions of the United States. Conclusion: The limited number of headache infusion centers overall may contribute to the limited ability of headache infusion centers to prevent ED migraine visits. Headache patients can have unpredictable headache onset, and most of the infusion practices surveyed appeared to adapt to this by offering infusions most days during a work week. However, this need for multiple days per week may also explain the most common reason for not having an infusion practice, which is insufficient staffing. Various treatment paradigms are implemented by different practitioners, and future studies will have to focus on investigation of best practice. [ABSTRACT FROM AUTHOR]

• Time spent at home increased 1.2–1.9 h per day during the pandemic. • 1-member households spent less time at home. • 2 + member households spent more time at home. • Office and kitchen space usage increased during the pandemic. • Changes to occupancy have strong implications on home energy use. Many aspects of the daily lives of those living in the United States were substantially impacted by the COVID-19 pandemic in the year 2020. A broad diversity of measures was implemented to curb the spread of the virus, many of which included adjustments to where and how people worked, went to school, and otherwise conducted their daily lives compared to pre-pandemic times. This has impacted how residential buildings are used, how much time people spend in their homes, and as a result, how much energy these buildings consume. The main objective of this study is to analyze, at a national scale, the differences in the occupancy schedules and activities conducted in homes in the U.S., as compared to pre-pandemic. 15 years of American Time Use Survey and Current Population Survey data, from 2006 to 2020, was used in this study to analyze the occupancy schedules for both pandemic (2020) and pre-pandemic (2006–2019) times. These impacts were also analyzed with respect to variables including, weekday/weekend, month of the year, age of the occupants, household income, and household size. The impact of the pandemic on occupant schedules were most substantial in the initial months, whereas as the months progressed, these occupancy profiles slowly changed. Across 2020, people spent, on average, 8 % more time (1.9 h) in their home on weekdays, and 3–6 % (1.2 h) on weekend days. The percentage of time spent for different activities and locations within homes were also studied. For 1-member households, their time spent at home decreased whereas for 2-, 3-, and 4- member households, they spent more time at home. Overall, people spent around 45% more time doing office- and work-related activities at home compared to pre-pandemic, which is likely due to increased remote working and schooling. This research helps to improve the understanding of the occupancy presence and absence profiles in U.S. residential buildings due to the pandemic and provides new insights as to modified profiles for researchers, building designers, and policy makers. [ABSTRACT FROM AUTHOR]

Regional air mobility refers to the transportation of passengers aboard small aircraft over short distances. These operations are challenging due to low passenger volumes, competing transportation modes, and high operating costs. Nevertheless, the recent convergence of new technologies for electric propulsion and autonomy brings the promise of step-changes in both operating efficiency and sustainability. The next challenge is to define concepts of operations that best use these advanced aircraft. We develop a modeling and optimization environment to address this challenge by first estimating the passenger demand for regional air mobility, and then by concurrently optimizing the fleet assignment and scheduling of these operations. This helps identify the optimum fleet composition and the network of routes that best serve this demand. The contribution of this research is the formulation of the optimization as a half-leg half-itinerary mixed-integer linear program using a hierarchical multi-objective approach that provides insights about trade-offs between profitability and emissions. We demonstrate the ability of this environment to solve large fleet assignment and scheduling problems to near optimality by applying it to the United States Northeast Corridor using a fleet of electric and hybrid-electric regional aircraft. Results highlight that the transition to an electrified fleet allows serving twice the number of communities currently served, while reducing the carbon emissions per passenger by fifty percent. With improving battery specific energy density, many more untapped markets can be served profitably and sustainably, and many smaller communities can get connected to the rest of the National Airspace System. • Optimization of fleet assignment and scheduling for regional air mobility. • Half-leg half-itinerary hierarchical multi-objective mixed-integer program. • Maximize profits and minimize carbon footprint, determine fleet composition. • Improved connectivity of underserved communities using electrified aircraft. • Regional air mobility demand modeling. [ABSTRACT FROM AUTHOR]

In this article we formulate and solve a model for choosing the grouping and ordering of state primary elections for a United States presidential election cycle. Our work fills a gap in the existing literature by defining a reproducible procedure for deriving an entire schedule of U.S. primary elections in a way that meets quantifiable goals of efficiency, equity, and effectiveness. Our model considers each state's attributes and gives higher priority to states whose voting and demographic profiles more closely reflect those of the county as a whole. It also clusters states into primary dates in a way that spreads the electorate relatively evenly over the primary season. Finally, the clustering process takes proximity into account, so that political campaigns can focus more effectively on a particular region without incurring excessive travel time. Since these criteria may conflict, the preferred ordering depends on the tradeoffs made among the goals. We highlight two solutions in which states with voting and demographic attributes close to those of the national profile are scheduled early on, while the geographic grouping and spread of voters over the schedule are significantly better than those of the status quo. [ABSTRACT FROM AUTHOR]

Despite considerable research on nursing turnover, few studies have considered turnover among nurses working in home health care. Using novel administrative data from one of the largest home health care organizations in the United States, this study examined turnover among home health nurses, focusing on the role of schedule volatility. We estimated separation rates among full-time and part-time registered nurses and licensed practical nurses and used daily visit logs to estimate schedule volatility, which was defined as the coefficient of variation of the number of daily visits in the prior four weeks. Between 2016 and 2019, the average annual separation rate of home health nurses was over 30%, with most separations occurring voluntarily. Schedule volatility and turnover were positively associated for full-time nurses, but not for part-time nurses. These results suggest that reducing schedule volatility for full-time nurses could mitigate nursing turnover in home health care.

Purpose/objective(s): The optimal delivery schedule for stereotactic body radiation therapy (SBRT) in treating Stage I non-small cell lung cancer (NSCLC) is unknown. This study utilized the National Cancer Database (NCDB) to examine daily vs. every other day (QOD) SBRT scheduling in the United States, including trends over time and association with survival.Materials/methods: The NCDB was used to identify patients with Stage I NSCLC treated with 3, 4, or 5 fraction SBRT between 2004-2016. Daily scheduling was defined as completing SBRT in 3, 4, or 5 days, respectively. QOD scheduling for 3, 4, and 5 fractions was defined as SBRT completion in 5-9, 8-12, and 10-14 days, respectively. Survival analysis was performed using the Kaplan-Meier method and Cox regression modeling. The multivariate model included age, gender, race, insurance status, median income, Charlson-Deyo comorbidity score, clinical T stage, histology, and facility type.Results: Of 15,269 patients who met study criteria, 3,927 (25.7%) received SBRT daily, and 11,342 (74.3%) received treatment QOD. A total of 4,993 (32.7%), 4,278 (28.0%), and 5,998 (39.3%) patients received 3, 4, and 5 fractions, respectively. The most common dose fractionations were 10 Gy x 5 (26.7%), 12 Gy x 4 (18.4%), and 18 Gy x 3 (17.3%). Factors significantly associated with daily SBRT scheduling included lower income (P < 0.0001), lower comorbidities (P = 0.0019), and treatment at academic/research programs (P < 0.0001). The use of daily SBRT scheduling decreased from a peak of 36.8% in 2007 to 21.7% in 2016, while QOD treatment increased from a nadir of 63.2% in 2007 to 78.3% in 2016 (both P < 0.0001). The use of 3-fraction SBRT decreased over time from 81.5% in 2004 to 23.4% in 2016, while 5-fraction SBRT increased from 3.7% in 2004 to 51.4% in 2016 (both P < 0.0001); there was no significant change in use of 4-fraction regimens (P = 0.6). The most common treatment schedule was 3 fractions QOD from 2004-2011 (25.5-70.4%), while more recently 5 fractions QOD was most prevalent from 2012-2016 (rising from 28.5% in 2012 to 41.6% in 2016). Median follow-up was 28.9 months, and median survival was 38.3 months. Median survival for patients who received daily SBRT was 37.9 months vs. 38.4 months for QOD (P = 0.4). On multivariate analysis, no difference was found in overall survival between daily vs. QOD scheduling (hazard ratio [HR] 0.99 [95% confidence interval 0.94-1.04]; P = 0.6). With 3-fraction SBRT, QOD treatment was associated with improved survival vs. daily treatment (HR 0.91 [0.84-0.98]; P = 0.02). With 5-fraction SBRT, QOD treatment was associated with worse survival vs. daily treatment (HR 1.11 [1.02-1.22]; P = 0.02).Conclusion: In this NCDB analysis, QOD SBRT schedules were more frequently used to treat Stage I NSCLC than daily regimens by a factor of 3:1. QOD scheduling increased over time, as did 5-fraction treatments, with 5-fraction QOD becoming the most common dose schedule after 2012. No clear association was found between daily vs. QOD scheduling and overall survival. [ABSTRACT FROM AUTHOR]

Although most hospitals in the United States provide medical services in English, a significant percentage of the U.S. population uses languages other than English. Mostly, the interpreting department in a hospital finds interpreters for limited English proficiency (LEP) patients, including inpatients, outpatients, and emergency patients. The department employs full-time and part-time interpreters to cover the demand of LEP patients. Two main challenges are facing an interpreting department: 1) there are many interpreting agencies in the market in which part-time interpreters can be chosen from. Selecting a part-time interpreter with the best service quality and lowest hourly rate makes the scheduling process difficult. 2) the arrival of LEP emergency patients must be predicted to make sure that LEP emergency patients are covered and to avoid any service delay. This paper proposes a framework for scheduling full-time and part-time interpreters for medical centers. Firstly, we develop a prediction model to forecast LEP patient demand in the emergency department (ED). Secondly, we develop a multi-objective integer programming (MOIP) model to assign interpreters to inpatient, outpatient, and emergency LEP patients. The goal is to minimize the total interpreting cost, maximize the quality of the interpreting service, and maximize the utilization of full-time interpreters. Various experiments are conducted to show the robustness and practicality of the proposed framework. The schedules generated by our model are compared with the schedules generated by the interpreting department of a partner hospital. The results show that our model produces better schedules with respect to all three objectives., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.)