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Background: Oral chemotherapy nonadherence is a challenge in clinical oncology. During therapy for acute lymphoblastic leukemia (ALL), poor adherence to 6-mercaptopurine (6MP) increases relapse risk. Clinically significant nonadherence is reported in 30% of children treated for ALL on Children's Oncology Group (COG) trials. Whether nonadherence rates vary across regimens with different treatment schedules and modes of administration is unknown. Methods: We conducted an exploratory, cross-sectional survey study on parents of children (1–18 years) receiving continuation therapy on, or as per Dana-Farber Cancer Institute (DFCI) ALL Consortium Protocol 11-001. Treatment required weekly visits to the clinic and 14 days of oral 6MP every 3 weeks. Survey assessed self-reported sociodemographics, medication-taking, chemotherapy comprehension, and 6MP adherence; adherence survey items were developed from published surveys. Patients were grouped as nonadherent if they endorsed missing one 6MP dose during the last cycle, or more than one dose during prior cycles, for nonmedical reasons. Results: Sixty-two families completed the surveys, all of whom had evaluable adherence data. In total, 25% of patients met the study definition of nonadherence. Twenty-three percent reported that it was “not easy” to follow administration guidelines around the dairy intake and 57% requested more teaching and educational resources. Conclusion: Self-reported nonadherence to oral 6MP in the DFCI ALL Consortium is high, with rates similar to those observed in the COG. This suggests that the additional contact during weekly infusions on the DFCI is insufficient to address barriers affecting oral chemotherapy adherence.

This research monetizes the access benefits of making shared autonomous vehicles (SAVs) available to residents of Texas’ Dallas-Fort Worth metroplex in the U.S. Residents’ willingness to pay for SAV access under different fares and modes was estimated and compared across the region’s 5,386 traffic zones, with emphasis on those housing the regions’ most vulnerable or access-limited travelers. Assuming a $0.50/mi SAV fare, the average per-person-trip benefit is estimated to be $0.64 per trip. With $0.50/mi SAV fare, HV mode share will be reduced from 92.4% to 40.3%, while SAV will take 55.8% of the share. However, if HVs are then disallowed (removed from everyone s mode choice set) after $0.50/mile SAVs have been added, the average net impact is estimated to be −$0.31 per trip, across the metroplex. If HVs were to be replaced by access to SAVs, the impacts are positive, with region-wide average access benefits ranging from $0.16 to $0.33 per trip, depending on the SAV fare, and urban zones have a greater access benefit than rural zones with low SAV fare. Vulnerable populations and their neighborhoods were identified based on the share of persons living below the poverty level, income per capita, share of persons aged 65 years or older, those with disabilities, those owning no vehicle, and share of persons from a racial minority group. Results suggest that the access benefits of SAVs will be higher in locations/neighborhoods housing more vulnerable populations, but some vulnerabilities (e.g., those over age 65) results in lower levels of access improvement. Across those zones with highest shares of vulnerable persons, the range of differences in welfare impacts, from adding SAVs to travelers mode choice sets, widened as fares rose. As is true with many innovations, careful attention to disadvantaged groups and thoughtful policy (via smart contracting and SAV-user subsidies by public agencies, for example) can better ensure valuable access improvements for those with limited mobility and resources.

This article wonders how reconsidering conceptions of time in research with children could expand and complicate notions of childhoods. Drawing on Karen Barad’s (2016, 2018) writing on temporal entanglements alongside Katherine Stockton’s (2009) discussion of growing sideways, research is reviewed and data from a participatory study reexamined. These divergent notions of temporality blur boundaries between childhood and adulthood and privilege perspectives often marginalized. The conclusion discusses implications of temporal orientations when researching with children including conceptions of pandemic time.

Vehicle electrification delivers fast decarbonization benefits by significantly improving vehicle efficiency and relying on less carbon-intense feedstocks. As power grids transition away from carbon-intensive generation and battery energy density improves, the transportation sector’s greenhouse gas savings may deliver upwards of a 75% reduction in current carbon footprint for many nations. Actual savings depend on many variables, like power grid feedstocks, charging rates and schedules, driver behavior, and weather. A special synergy between power and transportation sectors comes via managed charging and second-life battery uses for energy storage systems. This paper reviews the added carbon and energy savings that can come from these two strategies. If charging stations are widely available at one’s destination, utility-controlled managed charging could reduce emissions from electric vehicle charging by one-third. Downcycling electric vehicle batteries for energy storage can also lower peaker power plant use, avoid curtailment of renewable feedstocks, and lessen households’ power-based carbon footprints by half—or contribute up to 5% of grid power capacity.

Objective: An open trial tested the feasibility, acceptability, and preliminary effectiveness of a behavioral sleep intervention in adolescents with ADHD. Method: Fourteen adolescents (ages 13–17 years; 50% male) with ADHD and co-occurring sleep problems received the cognitive-behavioral-based Transdiagnostic Sleep and Circadian Intervention for Youth (TranS-C). Adolescent, parent, and teacher ratings, actigraphy, and daily sleep diaries were collected at pre-intervention, post-intervention, and 3-month follow-up. Results: Adolescents experienced moderate to large improvements in sleep, mental health symptoms, and daily life executive functioning from pre-treatment to post-treatment, and improvements were generally maintained at 3 months. Pre-intervention, 71.4% of adolescents were classified as poor sleepers and this was reduced to 21.4% and 28.6% at post-treatment and follow-up, respectively. Conclusion: This study provides strong preliminary evidence that TranS-C improves sleep and associated outcomes in adolescents with ADHD and co-occurring sleep problems. A randomized controlled trial is needed to rigorously test the efficacy of TranS-C in this population.

Background In the United States, alcohol use disorder (AUD) is common and costly but substantially undertreated. Rurality is an important determinant of health that may influence receipt of evidence-based alcohol-related care. In a large, national sample of Veterans Health Administration (VA) patients with AUD with documented and non-Hispanic Black, Hispanic, or non-Hispanic White race/ethnicity, we examine whether meeting national Healthcare Effectiveness Data and Information Set (HEDIS) quality measures for specialty addictions care and receiving evidence-based medications for AUD differs across patients living in urban, large rural, and small rural areas. Methods VA electronic health record data were used to identify all patients with AUD documented in Fiscal Year 2012. Rurality was measured using a three-category rural and urban commuting area (RUCA) classification linked to patient zip code. Logistic regression models with clustered standard errors—iteratively adjusted for hypothesized confounders—were used to estimate the likelihood and marginal probabilities of receiving care for patients living in small and large rural areas, relative to urban areas. Primary outcomes included HEDIS initiation (any visit within 14 days of initial AUD visit after a 60-day period of no treatment), HEDIS engagement (2 or more AUD visits within 30 days of HEDIS initiation visit) and having any filled prescription for AUD medications (naltrexone, disulfiram, acamprosate, or topiramate). Results For all outcomes, patients living in large and small rural areas had a lower likelihood of receiving evidence-based AUD treatment than patients living in urban areas (all p-values < 0.05); differences in marginal probabilities across groups were relatively small. Conclusions In this national sample of VA patients with AUD, those living in more rural areas were less likely to receive evidence-based treatment for AUD than those living in urban areas. Further research is needed to investigate strategies to increase receipt of specialty care and pharmacotherapy in more rural areas.

Superficial siderosis refers to hemosiderin deposition along the pial surface of the brain and spinal cord. It results from chronic and repetitive low-grade bleeding into the subarachnoid space. Dural tears are a common cause of superficial siderosis. Although such tears typically occur in the spine, dural tears can also occur in the posterior fossa. In many cases, posterior fossa dural tears are iatrogenic, and patients may present with neuroimaging evidence of postoperative pseudomeningoceles. We present a case of superficial siderosis caused by a persistent posterior fossa dural leak. The patient presented with superficial siderosis 30 years after a Chiari I malformation repair. A pinhole-sized dural tear was identified preoperatively using computed tomography cisternography. The dural defect was successfully repaired. An additional small tear that was not seen on imaging was also identified at surgery and successfully repaired.

An increasing number of corporations and workplaces are providing flexible working hours or flextime for employees, which is expected to reduce congestion by redistributing the temporal pattern of commuters’ departure time. This study examines the impact of flextime on departure time choice using a Bayesian continuous-time hazard duration model. The model accommodates the time-varying effect of covariates and unobserved heterogeneity. Results from the Austin Household Travel Survey collected between 2017 and 2018 show that workers who have a flextime option choose to leave later, with a predominant effect deterring morning peak departures. Other trip and individual-specific variables, such as travelers’ job type, trip duration, number of trips during the travel day, and household income, are found to have significant impacts on departure time choice. The results also show that flextime is more effective in shifting the departure time for retail and service sector employees, for those whose journeys are longer, and for those who perform more daily activities. The findings of this study support the theory that implementing such policies may ease congestion by staggering the travel demand from peak to off-peak hours.

Long-distance (LD) travel accounts for over 30% of person-trip miles, with important energy and emissions impact. LD business travel can often be replaced by remote participation, so targeting such trips for cost, time, and emissions savings may be a wise strategy for protection of the climate, budgets, and human health. To appreciate Americans’ LD travel choices better, a 73-question online survey was conducted in 2019 that captured 2,327 LD (over 100 mi each way) trips made by 929 respondents during the previous 12 months, of which 490 round trips were for business purposes. Predictive models for LD trips per adult per year, overnights, LD travel times, and willingness to participate remotely and/or purchase carbon offsets for those trips were developed using respondents in Austin only. As expected, those educated to degree level tend to travel more often, for both business and nonbusiness purposes; everything else is constant. People who undertake LD travel more frequently are more likely to spend less time in transit/en route. Single people or those from large households educated to degree level are more likely to be willing to pay for the carbon emissions produced by their flights. Out of the 298 LD business trips made by Austinites, remote participation is possible for approximately a quarter, and the respondents involved are willing to participate remotely in 44% of those trips. In other words, Austinites appeared willing to participate remotely in slightly over 10% of their business trips overall, at least before the COVID-19 pandemic. This is definitely not enough to address climate change concerns as a result of carbon emissions from LD travel.

A scheduling algorithm is developed for optimal planning of large-scale, complex evacuations to minimize total delay plus travel time across residents. The algorithm is applied to the eight-county Houston-Galveston region and land use setting under the 2017 Hurricane Harvey scenario with multiple destinations. Autonomous vehicle (AV) use under central guidance is also tested, to demonstrate the evacuation time benefits of AVs. Higher share of AVs delivers more efficient evacuation performance, thanks to greater reliability on evacuation order compliance, lower headways, and higher road capacity. Furthermore, 100% AV use delivers lower overall evacuation costs and network clearance times and less uncertainty in travel times (via lower standard deviation in). Based on evaluations of different evacuation schedules, a 50% compressed evacuation time span resulted in longer travel times and network congestion. A 50% longer evacuation time span reduced residents' total travel time and network congestion, but increased the evacuation cost. As expected, evacuation efficiency falls when evacuees do not comply with evaucation schedules. Large shares of AVs will not be possible in the near future, so methods to enhance evacuees' compliance behavior (e.g., enforced and prioritized evacuation orders) should be considered until a meaningful level of AV technical maturity and penetration rate is available. This paper demonstrates the benefits of scheduled departure times, AV use, and evacuation order compliance, which help balance conflicting objectives during emergencies.

Introduction There is increased interest in the use of artificial intelligence-based (AI) software packages in the evaluation of neuroimaging studies for acute ischemic stroke. We studied whether, compared to standard image interpretation without AI, Brainomix e-ASPECTS software improved interobserver agreement and accuracy in detecting ASPECTS regions affected in anterior circulation LVO. Methods We included 60 consecutive patients with anterior circulation LVO who had TICI 3 revascularization within 60 minutes of their baseline CT. A total of 16 readers, including senior neuroradiologists, junior neuroradiologists and vascular neurologists participated. Readers interpreted CT scans on independent workstations and assessed final ASPECTS and evaluated whether each individual ASPECTS region was affected. Two months later, readers again evaluated the CT scans, but with assistance of e-ASPECTS software. We assessed interclass correlation coefficient for total ASPECTS and interobserver agreement with Fleiss’ Kappa for each ASPECTS region with and without assistance of the e-ASPECTS. We also assessed accuracy for the readers with and without e-ASPECTS assistance. In our assessment of accuracy, ground truth was the 24 hour CT in this cohort of patients who had prompt and complete revascularization. Results Interclass correlation coefficient for total ASPECTS without e-ASPECTS assistance was 0.395, indicating fair agreement compared, to 0.574 with e-ASPECTS assistance, indicating good agreement (P Conclusions Use of Brainomix e-ASPECTS software resulted in significant improvements in inter-rater agreement and accuracy of ASPECTS score evaluation in a large group of neuroradiologists and neurologists. e-ASPECTS software was more predictive of final infarct/ASPECTS than the overall group interpreting the CT scans with and without e-ASPECTS assistance.

Understanding the preferences for new and future transportation technologies is important to ensure an efficient and equitable future transportation system. A survey was conducted of Americans’ preferences for several such technologies. Americans are concerned about vehicle range and charging station availability for electric vehicles (EVs) and hesitant about autonomous vehicle (AV) safety. Opinions about many transportation technologies, such as vertical takeoff and landing (i.e., air taxis), shared parking, and air-drone delivery are mixed. These less familiar technologies require continued tracking of preferences. A 55% increase is estimated in the probability of an individual choosing a battery electric vehicle (BEV) pickup truck if its fuel economy increases by about 9%. This result supports a market for BEV pickup trucks currently under development by many automakers. The preference for vehicle autonomation appears to depend on the use case. Driving task automation is preferred by residents of low-density, car-dependent areas where long commutes are common. In contrast, automated parking technologies are favored by those living in denser communities. Intermittent bus lanes are favored by those living in high population density areas, but not among those in areas with high shares of zero-vehicle households. These results provide indications of where to direct future research in the field.

Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region’s 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD.

Shared fleets of fully automated vehicles (SAVs) coupled with real-time ride-sharing to and from transit stations are of interest to cities and nations in delivering more sustainable transportation systems. By providing first-mile last-mile (FMLM) connections to key transit stations, SAVs can replace walk-to-transit, drive-to-transit, and drive-only trips. Using the SUMO (Simulation of Urban MObility) toolkit, this paper examines mode splits, wait times, and other system features by micro-simulating two fleets of SAVs providing an FMLM ride-sharing service to 10% of central Austin’s trip-makers near five light-rail transit stations. These trips either start or end within two geofenced areas (called automated mobility districts [AMDs]), and travel time and wait time feedbacks affect mode choices. With rail service headways of 15 min, and 15 SAVs serving FMLM connections to and from each AMD, simulations predict that 3.7% of the person-trip-making will shift from driving alone to transit use in a 3 mi × 6 mi central Austin area. During a 3-h morning peak, 30 SAVs serve about 10 person-trips each (to or from the stations), with 3.4 min average wait time for SAVs, and an average vehicle occupancy of 0.74 persons (per SAV mile-traveled), as a result of empty SAV driving between riders. Sensitivity analysis of transit headways (from 5 to 20 min) and fleet sizes (from 5 to 20 vehicles in each AMD) shows an increase in FMLM mode share with more frequent transit service and larger fleet size, but total travel time served as the biggest determinant in trip-makers’ mode share.

This study seeks smart credit-based congestion pricing (CBCP) solutions for maximally improving travelers’ welfare by varying toll levels and locations across the Austin, Texas network. Scenarios evaluated include selecting links with maximum delays by variably tolling bridges and by recognizing congestion externalities across all links. Travel demand models deliver inputs for normalized logsum differences to quantify and compare consumer surplus changes across traveler types, around the region. Results suggest limited tolling locations under four broad times of day can do more harm than good, unless travelers shift out of the PM and AM peak periods or revenues are returned to travelers as credits. When using CBCP across all congested links at congested times of day (with 10% of revenues retained to cover system administrative costs), an average net benefit of $1.61 per licensed driver per weekday is estimated, with almost all travelers benefiting. For example, 95% of the traffic analysis zones’ lowest value of travel time (VOTT) group (VOTT1 = $5/hour) are expected to benefit from the CBCP policy. Tolling at twice the difference between marginal social cost and average travel cost (on each subset of congested links) appears to benefit more people, although tolling high on various links adds to congestion elsewhere. For example: tolling Austin’s highest-delay-producing or “top 500” links is estimated to benefit 98.5% of the zones’ highest VOTT (VOTT5 = $45/hour) travelers, while raising vehicle-miles traveled by just 0.8% (as a result of more circuitous, congestion- and toll-avoiding travel).

High costs of owning fully-automated or autonomous vehicles (AVs) will fuel the demand for shared mobility, with zero driver costs. Although sharing sounds good for the transport system, congestion can easily rise without adequate policy measures. Many or all public transit lines will continue to exist, and carefully-designed policies can be implemented to make good use of fixed public assets, like commuter- and light-rail lines. In this study, a shared AV (SAV) fleet is analyzed as a potential solution to the first-mile-last-mile (FMLM) problem for access to and from public transit. Essentially, SAVs are analyzed as collector-distributor systems for these mass-movers and compared with a door-to-door (D2D) service. Results from an agent-based simulation of Austin, Texas, show that SAVs have the potential to help solve FMLM transit problems when fare benefits are provided to transit users. Restricting SAV use for FMLM trips increases transit coverage, lowers average access and egress walking distance, and shifts demand away from park-and-ride and long walk trips. When SAVs are available for both D2D use and FMLM trips, high SAV fares help maintain transit demand, without which the transit demand may decrease significantly, affecting the transit supply and the overall system reliability. Policy makers and planners should be wary of this shift away from transit and may be able to increase transit usage using policies tested in this study.

A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.

Background: We sought to quantify the efficiency and acceptability of Internet-based recruitment for engaging an especially hard-to-reach cohort (college-students with type 1 diabetes, T1D) and to describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools inclusive of participant safety and validity concerns. Method: We recruited youth (ages 17-25 years) with T1D via a variety of social media platforms and other outreach channels. We quantified response rate and participant characteristics across channels with engagement metrics tracked via Google Analytics and participant survey data. We developed decision rules to identify invalid (duplicative/false) records (N = 89) and compared them to valid cases (N = 138). Results: Facebook was the highest yield recruitment source; demographics differed by platform. Invalid records were prevalent; invalid records were more likely to be recruited from Twitter or Instagram and differed from valid cases across most demographics. Valid cases closely resembled characteristics obtained from Google Analytics and from prior data on platform user-base. Retention was high, with complete follow-up for 88.4%. There were no safety concerns and participants reported high acceptability for future recruitment via social media. Conclusions: We demonstrate that recruitment of college students with T1D into a longitudinal intervention trial via social media is feasible, efficient, acceptable, and yields a sample representative of the user-base from which they were drawn. Given observed differences in characteristics across recruitment channels, recruiting across multiple platforms is recommended to optimize sample diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms.

Improved traffic management techniques are needed to reduce congestion on road networks, especially as “driving” is made easier, through self-driving vehicles. In this paper, reactive congestion pricing varies toll rates based on recent congestion levels, and automated vehicles are added to the conventional traffic mix for evaluation of evolving travel conditions. As expected, drivers with higher values of travel time (VOTT) are more likely to use the tolled route than drivers with lower VOTT, and tolled-route speeds rose (about 4%) while speeds on non-tolled road segments fell (about 15%). Thanks to traveler sorting, net benefits exceeded $600 per hour in all scenarios, using a very small (toy) network. Toll revenues can be distributed uniformly among travelers (resulting in credit-based congestion pricing) or invested in improving bottlenecks and alternative modes. Rising shares of automated vehicles (from 0% to 50% and 100%) also improved outcomes.

Background Medical–legal partnerships integrate lawyers into health care to identify and address legal problems that can create and perpetuate disparities in health for patients and their families. They have previously been utilised for patients who are at high-risk of being disadvantaged such as the elderly, the disabled and those affected by chronic diseases. We have used a partnership to address the legal needs of patients with acute, critical illness including major trauma. Method In 2007, a free, comprehensive legal advice service was established at University Hospital Southampton NHS Foundation Trust. The service is bound by strict guidelines which have been endorsed by NHS England. The legal service is specifically prevented from acting against the NHS. A retrospective analysis of the service over a period of 11 years was undertaken to look at the range of legal advice sought. Where a potential compensation claim against a third party was identified, the percentage of cases where the legal service was instructed was noted and the outcome for those cases was examined in further detail. Results Five hundred and fifty-one patients and or their families have been referred to the legal service. Of these, 343 had sustained major trauma. Over 2300 hours of free legal advice were provided on non-compensation issues, primarily related to welfare benefits, local authority assistance, obtaining power of attorney or seeking Deputyship from the Court of Protection and claims against existing insurance policies. Two hundred and seventy-five of the 551 patients (50%) were found to have a potential compensation claim against a third party. The legal service was instructed to pursue a claim in 82 cases. Interim payments of nearly £13 million were provided and £128 million of compensation has been awarded in 51 cases that have been settled. Discussion Medical–legal partnerships are well-established in the USA. We have demonstrated that in UK, there is a demand for early legal advice for patients who have sustained critical illness including major trauma. More data are required to identify the rehabilitation outcomes for patients who have received legal support. A similar medical–legal partnership should be considered at every acute NHS Trust.

Accountability-era reforms have promulgated a discourse of crisis and a looming threat for public schools. While these threats negatively affect all, this paper focuses on how schools’ responses to these threats shape novice Teach for America Corp Members’ (CM) socialization. By analyzing nearly 6,000 daily reflections from 38 CMs about their experiences during a high-stakes cheating scandal, we argue that schools’ responses indicated a threat rigidity response that allowed what we call the mesosystem of transgressions to occur. Our implications present ways to (a) mitigate threats inflicted on schools through unjust policies and practices and (b) enact equitable public schools.

Background: People living with HIV (PLWH) are at risk for falls due to polypharmacy, unhealthy substance (risky alcohol and/or illicit drug) use, low physical activity, and frailty combined with typical age-related physical changes. Fall prevention is needed to reduce the morbidity related to falls and fractures, however, there is a paucity of data on the design of a fall prevention intervention and whether it can be delivered virtually. We describe the protocol of a pilot randomized trial of a virtual occupational therapy fall prevention intervention for people with HIV at high risk for falls and recent alcohol and/or drug use. Method: PLWH will be recruited from the Boston ARCH 4F Cohort study, an observational study of PLWH to examine the impact of alcohol on falls. Trial participants will be randomized to either an occupational therapy-led fall prevention intervention or provided with written education about fall prevention and alcohol use (control). The 10-week fall prevention intervention was based upon results from qualitative interviews with PLWH about falls and will consist of weekly virtual group sessions, home exercises and phone-check-ins, delivered by occupational therapists. The primary outcome measures will be number of groups attended and a participant-completed satisfaction survey. Change in number of falls, alcohol and other drug use, and physical functioning will be examined. Discussion: A virtual occupational therapy fall prevention intervention addresses the emerging concern of fall risk in PLWH and alcohol use. This pilot study will provide preliminary estimates of fall-related outcomes as well as feasibility of study procedures for a larger trial. ClinicalTrials.gov Identifier: NCT04804579. Boston University Protocol Record H-41041

This study demonstrates three methods for uncertainty propagation in transportation and land-use models (LUMs): Local Sensitivity Analysis with Interaction (LSAI), Monte Carlo (MC), and Bayesian Melding (BM). Two case-study settings are used to illustrate how these methods work, allowing for inter-method comparisons. LSAI can provide the sign of change implied by changes in model inputs, the relative importance of changes in different inputs, and a decomposition of changes in outputs due to the impact of inputs’ individual and interactive. LSAI is limited to relatively small-size problems because its computing time rises exponentially with the number of (groups of) inputs. Moreover, LSAI obtains only point estimates, while MC and BM methods can deliver entire distributions of each output through an understanding of the uncertainty in all model inputs and parameters. MC delivers each output’s distribution and requires hundreds of samples, especially for more accurate results. Fortunately, MC methods are especially useful for high-dimensional problems because convergence rates are not a function of model dimensionality and errors depend only on sample size and input uncertainties. BM delivers posterior distributions for model outputs, using prior probability distributions and likelihoods of inputs and parameters, along with validation of/comparison to intermediate model outputs. A BM approach can be extremely expensive, in terms of computing time, since it requires several hundred model runs.

Nested within a 5-year examination of Teach for America (TFA) corps members’ experiences with learning to teach, this article focuses on the salient theme of “Reform Models” found in data collected when a highly publicized testing cheating scandal was breaking in this urban district. Using constructivist grounded theory, researchers analyzed 5,897 daily reflections written during a year-long, initial certification course. Rigorous, comprehensive, and collaborative data analysis utilizing a multirater/multistage/multiyear process, including ATLAS.ti, researchers’ memos, audit trails, and emic perspectives of participant–researchers, assured trustworthiness. Analysis revealed federal accountability policies and education reforms that had profound and negative influences on literacy curriculum, pedagogy, teaching, learning, and classroom practice.