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We investigate two research questions using a recent statewide transition from paper to computer-based testing: first, the extent to which test mode effects found in prior studies can be eliminated; and second, the degree to which online and paper assessments offer different information about underlying student ability. We first find very small mode effects for a more recent transition in Massachusetts. Second, we investigate the predictive evidence of validity for paper and online tests for predictions of future test scores and grades. We generally find minimal differences for the extent to which scores on paper tests can differentially predict future online versus paper test scores. Finally, online and paper test scores are similarly predictive of future grade point average. We conclude that the online test penalty can vary substantially by test and that extreme care should be taken when administering online tests to some students and paper tests to others.

We investigate two research questions using a recent statewide transition from paper to computer-based testing: first, the extent to which test mode effects found in prior studies can be eliminated; and second, the degree to which online and paper assessments offer different information about underlying student ability. We first find very small mode effects for a more recent transition in Massachusetts. Second, we investigate the predictive evidence of validity for paper and online tests for predictions of future test scores and grades. We generally find minimal differences for the extent to which scores on paper tests can differentially predict future online versus paper test scores. Finally, online and paper test scores are similarly predictive of future grade point average. We conclude that the online test penalty can vary substantially by test and that extreme care should be taken when administering online tests to some students and paper tests to others. [ABSTRACT FROM AUTHOR]

This article refers to: RETRACTED ARTICLE: Nathan John and David Deuel, Disability in Mission: The Church's Hidden Treasure. (Peabody, Publishers Massachusetts: John Hendrickson, 2019). 167 pp. Paper, $19.95. We, the Author, Editors and Publisher of Journal of Disability & Religion, have retracted the following book review: Sunil Kathramalla (2021) Nathan John and David Deuel, Disability in Mission: The Church's Hidden Treasure. (Peabody, Publishers Massachusetts: John Hendrickson, 2019). 167 pp. Paper, $19.95., Journal of Disability & Religion, DOI: 10.1080/23312521.2021.1901639 The book review was mistakenly published as a duplicate of the below book reviews by the same author, and the author has agreed to retract. Kathramalla, S. (2020), John, Nathan and Deuel, David, eds. Disability in Mission: The Church's Hidden Treasure. Peabody, Massachusetts: John Hendrickson, 2019. 167 pp. International Review of Mission, 109: 144–145. Kathramalla, S. (2019), Disability in Mission: The Church's Hidden Treasure. Asian American Theological Forum, 8. We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions. The retracted book review will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as "Retracted." [ABSTRACT FROM AUTHOR]

Precise road segmentation is an essential part of many applications related to road information extraction from remote sensing data. The effect of color space on road detection has rarely been studied. In this paper, the effects of different color spaces of aerial images and multitask learning methods were experimented on road segmentation using three deep convolutional neural networks, UNet, DenseU-Net, and RoadVecNet. The color spaces included RGB, HSV, LAB, YCbCr, and YUV. The multitask learning methods adopted in this study involved utilizing multiple inputs, and multiple outputs. Multiple inputs were aerial images from the same area with different color spaces, and multiple outputs were road segmentation and road outline segmentation. As remote sensing data, National Land Survey of Finland's true orthophotos (from 2020), Massachusetts road imagery dataset, and Ottawa dataset were applied. Segmentation masks for National Land Survey of Finland's true orthophotos were extracted from Digiroad vectors with road width information. Road outline masks were generated from the segmentation masks. The studied neural networks were trained with the same data, learning rate, loss function, and optimizer for each color space, and pairs of color spaces. Multiple outputs were experimented with RGB color space. The comparative analysis assessed the performance of various neural networks across different color spaces using the F1-score metric. The experimental findings indicate that the choice of color space has little influence on the results of neural networks Deep learning methods can adapt to different color spaces well. In addition, the use of sharpening and edge enhancement augmentations had a slight effect on the results. [ABSTRACT FROM AUTHOR]

Background: To address the need for interventions for families with parents with mental illness, the evidence-based intervention Let's Talk about Children (LTC) was adapted in the context of adult mental health services in the United States and reframed as the ParentingWell Practice Approach. This study focuses on the early implementation phase of the adapted practice in Massachusetts. Methods: As part of the adaptation and implementation process, practitioners from provider agencies serving adults with mental illness were invited to participate in the ParentingWell Learning Collaborative (PWLC), which included in-person learning collaborative sessions and follow-up virtual coaching sessions. This paper focuses on data obtained during and in response to the PWLC virtual coaching sessions, from 29 participants. Specific research questions included: (1) What themes emerged in coaching sessions related to practitioners' experiences during the early implementation of the ParentingWell Practice Approach (2) In what ways are coaching sessions helpful to the practitioners as they implement the ParentingWell Practice Approach? Coaching sessions were recorded, and transcribed, and the data were analyzed qualitatively to identify early implementation themes. Practitioners completed feedback surveys online (which included Likert scale items and open-ended questions) following virtual coaching sessions to evaluate the usefulness of coaching sessions. Results: Coaching sessions reflected the following themes related to practitioners' experiences during the early implementation of ParentingWell: (1) practitioners identify and share concrete approaches to supporting parents; (2) practitioners reflect on parents' needs related to support, advocacy, problemsolving, and parenting skills; (3) practitioners reflect on their own personal experiences; and (4) practitioners' recognize the importance of self-care strategies for themselves and for parents served. Practitioners indicated that coaching sessions were useful in supporting the implementation of a new practice. Conclusion: This study provides preliminary evidence for the benefits of coaching in the implementation of ParentingWell. Future research will explore the impact of ParentingWell on outcomes for parents and families served. [ABSTRACT FROM AUTHOR]

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively. [ABSTRACT FROM AUTHOR]

In wind engineering, to accurately estimate the nonlinear dynamic response of structures while considering uncertainties of hurricanes, a suite of wind records representing the hurricane hazards of a given location is of great interest. Such a suite generally consists of a large number of hurricane wind records, which may lead to highly computational cost for structural analysis. To reduce the computational demand while still preserving the accuracy of the uncertainty quantification process, this paper proposes a machine learning approach to select a representative subset of all collected hurricane wind records for a location. First, hurricane wind records, which are expressed as time series with information that includes both wind speed and direction, are collected from a synthetic hurricane catalog. The high dimensional hurricane wind records are then compressed into a set of low dimensional latent feature vectors using an artificial neural network, designated as an autoencoder. The latent feature vectors represent the important patterns of wind records such as duration, magnitude, and the changing of wind speeds and directions over time. The wind records are then clustered by applying the k -means algorithm on the latent features, and a subset of records is selected from each cluster. The wind records selected from each cluster are those whose latent feature points are closest to the centroid of all latent feature points in that cluster. In order to do regional analysis while taking into account that the hurricane wind records are site-specific, this paper suggests that a region can be discretized into a set of grids, with the proposed hurricane selection approach applied to each grid. This procedure is demonstrated using Massachusetts as a testbed. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932. [ABSTRACT FROM AUTHOR]

Automatic extraction of building contours from high-resolution images is of great significance in the fields of urban planning, demographics, and disaster assessment. Network models based on convolutional neural network (CNN) and transformer technology have been widely used for semantic segmentation of buildings from high resolution remote sensing images (HRSI). However, the fixed geometric structure and the local receptive field of the convolutional kernel are not good at global feature extraction, and the transformer technique with self-attention mechanism introduces computational redundancies and extracts local feature details poorly in the process of modeling the global contextual information. In this paper, a dual-branch fused reconstructive transformer network, DFRTNet, is proposed for efficient and accurate building extraction. In the encoder, the traditional transformer is reconfigured by designing the local and global feature extraction module (LGFE); the branch of global feature extraction (GFE) performs dynamic range attention (DRA) based on the idea of top-k attention for extracting global features; furthermore, the branch of local feature extraction (LFE) is used to obtain fine-grained features. The multilayer perceptron (MLP) is employed to efficiently fuse the local and global features. In the decoder, a simple channel attention module (CAM) is used in the up-sampling part to enhance channel dimension features. Our network achieved the best segmentation accuracy on both the WHU and Massachusetts building datasets when compared to other mainstream and state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The aim of the paper is to derive minimization algorithms based on the Nesterov accelerated gradient flow [Y. Nesterov, Gradient methods for minimizing composite objective function. Core discussion paper, (2007). Available at ; Y. Nesterov, A method of solving a convex programming problem with convergence rate $ \mathcal {O}(1/k^2) $ O (1 / k 2). In Doklady Akademii Nauk, Vol. 269, Russian Academy of Sciences, 1983, pp. 543–547; Y. Nesterov, Introductory Lectures on Convex Optimization: A basic course, Kluwer Academic Publishers, Massachusetts, 2004.] to compute the ground state of nonlinear Schrödinger equations, which can potentially include a fractional laplacian term. A comparison is developed with standard gradient flow formulations showing that the Nesterov accelerated gradient flow has some interesting properties but at the same time finds also some limitations due to the nature of the problem. A few simulations are finally reported to understand the behaviour of the algorithms and open the path to further complicate questions that require more advanced studies concerning the application of the Nesterov accelerated gradient flow to nonlinear Schrödinger equations. [ABSTRACT FROM AUTHOR]

To meet 2050 decarbonization goals, Massachusetts will not be able to rely on carbon intensive energy sources (e.g. natural gas and gasoline) and hydrogen has been considered a replacement. To produce hydrogen without carbon emissions, renewable energy sources will be used to power electrolyzer stacks. However, renewable energy sources will also be in high demand for other energy sectors, such as automobiles and electrification. This paper estimates the amount of wind energy needed to replace natural gas with hydrogen and electrify automobiles. Comparisons are also made for a scenario in which heat pumps are used to replace natural gas. These energy sectors represent the bulk of energy consumed within Massachusetts and are of high interest to stakeholders globally. The analysis reveals the daunting amount of wind energy needed for replacement and that it is highly unlikely for hydrogen to replace natural gas in time to meet the state's climate goals. [ABSTRACT FROM AUTHOR]

Roadway area calculation is a novel problem in remote sensing and urban planning. This paper models this problem as a two-step problem, roadway extraction, and area calculation. Roadway extraction from satellite images is a problem that has been tackled many times before. This paper proposes a method using pixel resolution to calculate the area of the roads covered in satellite images. The proposed approach uses novel U-net and Resnet architectures called U-net++ and ResNeXt. The state-of-the-art model is combined with the proposed efficient post-processing approach to improve the overlap with ground truth labels. The performance of the proposed road extraction algorithm is evaluated on the Massachusetts dataset and it is shown that the proposed approach outperforms the existing solutions which use models from the U-net family. [ABSTRACT FROM AUTHOR]

Background: Engagement is recognized as an important factor in aphasia treatment response and outcomes, yet gaps remain in our understanding of engagement and practices that promote engagement from the client perspective. Aims: The purpose of this phenomenological study was to explore how clients with aphasia experience engagement during their inpatient aphasia rehabilitation. Methods & Procedures: An interpretative phenomenological analysis approach guided the study design and analysis. Data were collected through in‐depth interviews with nine clients with aphasia, recruited through purposive sampling, during their inpatient rehabilitation admission. Analysis was completed using a variety of analytic techniques including coding, memoing, triangulation between coders and team discussion. Outcomes & Results: The analysis revealed that for clients with aphasia in the acute phrase of recovery, the rehabilitation process resembles travelling on a journey through a foreign land. Successful engagement in the journey was accomplished when one had a therapist who served as a trusted guide and was able to be a friend, invested, adaptable, a co‐creator, encouraging and dependable. Conclusions & Implications: Engagement is a dynamic, multifaceted and person‐centred process involving the client, provider and rehabilitation context. Findings from this work have implications for measuring engagement, training student clinicians to be skilled facilitators in engaging their clients and implementing person‐centred practices that promote engagement within clinical settings. WHAT THIS PAPER ADDS: What is already known on the subject: Engagement is recognized as an important factor in rehabilitation treatment response and outcomes. Prior literature suggests that the therapist plays a critical role in facilitating engagement within the client–provider relationship. Communication impairments associated with aphasia may negatively impact a client's ability to develop interpersonal connections and participate in the rehabilitation process. There is a dearth of research directly exploring the topic of engagement in aphasia rehabilitation, particularly from the perspective of clients with aphasia. Capturing the client perspective can provide novel insights regarding practices to foster and maintain engagement in aphasia rehabilitation. What this paper adds to existing knowledge: This interpretative phenomenological study revealed that for individuals with aphasia in the acute phase of recovery, the rehabilitation process resembles travelling on a sudden and foreign journey. Successful engagement in the journey was accomplished when one had a therapist who served as a 'trusted guide' and was able to be a friend, invested, adaptable, a co‐creator, encouraging and dependable. Through the client experience, engagement is seen as a dynamic, multifaceted and person‐centred process involving the client, provider and rehabilitation context. What are the potential or actual clinical implications of this work?: The current study highlights the complexity and nuance of engagement within the rehabilitation context, which has implications for measuring engagement, training student clinicians to be skilled in engaging their clients and implementing person‐centred practices that promote engagement within clinical settings. It is necessary to recognize that client and provider interactions (and thus engagement) are embedded in and influenced by the broader healthcare system. With this in mind, a patient‐centred approach to engagement in aphasia care delivery cannot be achieved through individual efforts only and may require prioritization and action at the systems level. Future work is needed to explore barriers and facilitators to enacting engagement practices, in order to develop and test strategies to support practice change. [ABSTRACT FROM AUTHOR]

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements. [ABSTRACT FROM AUTHOR]

Road extraction from remote sensing images is significant for urban planning, intelligent transportation, and vehicle navigation. However, it is challenging to automatically extract roads from remote sensing images because the scale difference of roads in remote sensing images varies greatly, and slender roads are difficult to identify. Moreover, the road in the image is often blocked by the shadows of trees and buildings, which results in discontinuous and incomplete extraction results. To solve the above problems, this paper proposes a multiscale feature encoding and long-range context-aware network (MECA-Net) for road extraction. MECA-Net adopts an encoder–decoder structure and contains two core modules. One is the multiscale feature encoding module, which aggregates multiscale road features to improve the recognition ability of slender roads. The other is the long-range context-aware module, which consists of the channel attention module and the strip pooling module, and is used to obtain sufficient long-range context information from the channel dimension and spatial dimension to alleviate road occlusion. Experimental results on the open DeepGlobe road dataset and Massachusetts road dataset indicate that the proposed MECA-Net outperforms the other eight mainstream networks, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Neuropsychological assessment using the Boston Process Approach (BPA) suggests that an analysis of the strategy or the process by which tasks and neuropsychological tests are completed, and the errors made during test completion convey much information regarding underlying brain and cognition and are as important as overall summary scores. Research over the last several decades employing an analysis of process and errors has been able to dissociate between dementia patients diagnosed with Alzheimer's disease, vascular dementia associated with MRI-determined white matter alterations, and Parkinson's disease; and between mild cognitive impairment subtypes. Nonetheless, BPA methods can be labor intensive to deploy. However, the recent availability of digital platforms for neuropsychological test administration and scoring now enables reliable, rapid, and objective data collection. Further, digital technology can quantify highly nuanced data previously unobtainable to define neurocognitive constructs with high accuracy. In this paper, a brief review of the BPA is provided. Studies that demonstrate how digital technology translates BPA into specific neurocognitive constructs using the Clock Drawing Test, Backward Digit Span Test, and a Digital Pointing Span Test are described. Implications for using data driven artificial intelligence-supported analytic approaches enabling the creation of more sensitive and specific detection/diagnostic algorithms for putative neurodegenerative illness are also discussed. [ABSTRACT FROM AUTHOR]

The HEALing Communities Study (HCS) is a large-scale multisite study testing community engagement using coalition facilitation as an approach to addressing the worsening overdose crisis. Within community engagement, community-based participatory research (CBPR) principles guide researchers on best practices for working in partnership with communities, yet these principles have not been well researched in large, complex, multisite studies. This paper uses ethnographic methods to explore how coalitions operationalized CBPR principles during early coalition formation. Two coders independently analyzed 101 ethnographies from HCS coalition meetings in eight Massachusetts communities held between November 2019 and December 2020. Themes were developed through consensus between the coders, followed by group discussions among the authorship team. We found that mutual trust, shared goals, addressing power dynamics, meeting structure, and attending to the sociopolitical community context are critical elements that can either hinder or advance the use of CBPR principles in practice. These findings provide unique suggestions for future community-engaged multisite studies, and demonstrate the importance of research teams mitigating inherent power imbalances by acknowledging and creating spaces for community ownership. The findings also highlight the value of a community engagement facilitator (CEF) role, as well as strategies like transparency, uniting over shared interests, and bringing in a wide range of stakeholders when operationalizing CBPR principles. [ABSTRACT FROM AUTHOR]

This paper seeks to use eye-tracking emulation software to understand and forecast the human responses to signage and directional cues in Massachusetts, USA. Using eye-tracking emulation software, this study measured the unconscious visual responses people are projected to have to designs and layouts in new built environments, focusing on what makes streets most walkable. The study found differences between the way the brain takes in conventional automobile-oriented residential developments versus new urbanist layouts, with the former lacking key fixation points. The study's discoveries explain why new urbanist layouts promote walking effortlessly and conventional automobile-oriented residential developments cannot. [ABSTRACT FROM AUTHOR]

Boston's "Big Dig" construction project resulted in the excavation of multiple archaeological sites dating from the seventeenth to nineteenth centuries, including the Great House/Three Cranes Tavern in Charlestown, Massachusetts (USA). An otherwise unremarkable pit below the tavern foundation contained bones originally identified as a cat skeleton, which has subsequently been reidentified as a dog. This paper discusses site context, osteological evidence for the dog's reclassification, and the shifts in cultural meaning this may indicate. Employing an osteobiographical approach, it draws together points of connection between the modern skeletal assessment, a series of 1980s excavations, and the motivations of eighteenth-century tavern inhabitants. [ABSTRACT FROM AUTHOR]

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Building footprint information is crucial for comprehending global urban development processes. Deep learning algorithms have shown significant potential in building extraction from high spatial resolution imagery. However, the requirement for large-scale annotated data has been a limitation for applying deep learning methods to city-level or national-level building mapping. The dynamic change and distinct landscape variation of cities in different geographic locations further emphasizes the need for automatic building footprint extraction. In this paper, we propose a Self-supervised Knowledge Transfer (SKTrans) framework for extracting building footprints from unlabeled remote sensing images over large areas. To address building tone differences cross regions and time periods, a tone shift mechanism is introduced to reduce the contrast, brightness, and saturation differences between buildings. Furthermore, considering building style diversity across different cities, a knowledge integration module is proposed to develop a comprehensive depiction including tonal differences, multi-scale variance, structural attributes and semantic confusion between roads and buildings. The effectiveness of SKTrans is demonstrated on three public datasets of WHU, Massachusetts, and Inria. To further evaluate the feasibility of large-scale mapping for automatic building footprint, global generalization experiments cross regions and time periods, which is based on imagery from five countries of different continents were implemented. The comprehensive results demonstrate that SKTrans surpasses existing state-of-the-art methods, supporting the time-series city-level building mapping without labeled dataset. Additionally, the architectural landscape pattern changes can be quantitatively analyzed based on the generalization results to facilitate sustainable urban development. Overall, SKTrans provides a new insight to meet the needs of high-precision label-free building mapping and update from large-scale multi-style remote sensing images. [ABSTRACT FROM AUTHOR]

Objectives: In this study, we aimed to explore the oral and emotional health challenges experienced by a sample of refugees in Massachusetts across different stages of resettlement using a mixed methods approach. Methods: We collaborated with two Federally Qualified Health Centers to identify and recruit participants for either surveys (n = 69) or semi-structured interviews (n = 12). Data collection was conducted in 2018. We performed descriptive statistics using STATA 14, and analyzed the interviews using qualitative methods. Results: Overall, cost and lack of structure were the largest barriers identified for accessing dental care in participants' home and host countries. In the US, participants reported receiving state-provided public health insurance, but still experienced disrupted access to dental care due to coverage limitations. We identified several mental health risk factors that may affect participants' oral health, including trauma, depression, and sleeping problems. Despite these challenges, participants also identified areas of resilience and adaptability in both attitude and actions. Conclusions: The themes identified in our study suggest that refugees have attitudes, beliefs, and experiences that contribute to their perspectives on oral health care. While some of the reported barriers to access dental care were attitudinal, others were structural. Access to dental care in the US was reported to be structured and available, but with limited coverage issues. This paper underscores the oral and emotional health aspects of refugees for future considerations and planning of appropriate, affordable and cost-effective policies in the global health care systems. [ABSTRACT FROM AUTHOR]

Introduction: Academic libraries are facing two trends: the rising cost of journal subscriptions--the ongoing "serials crisis"--and the increasing availability of scholarly journal articles through free, open access channels. Methods: This study analyzed the bibliographies from 116 research projects created by students across disciplines at a small community college and classified citations to journal articles based on the simplest way students could have accessed them. Results: More than 60% of the scholarly journal articles that students used are available to them for free, and an additional 18% are available through state-sponsored database subscriptions available to any Massachusetts resident. Only 17% of the journal articles used were available to students solely through the library's database packages, and more than half of those articles were used by students in the associate degree of nursing (ADN) program. Discussion: These findings call into question the value of expensive subscription databases to community college libraries and suggest a pattern of diminishing return on investment and value. The trend toward cancellation of "big deal" journal packages at large institutions addresses similar concerns, although the dynamics are somewhat different at small institutions. Conclusion: The paper concludes by suggesting some steps that small academic libraries might take to adapt to this changing publishing environment, in the areas of collection development and information literacy instruction, in order to better serve students. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning technology have triggered radical progress in the autonomy of ground vehicles. Marine coastal Autonomous Surface Vehicles (ASVs) that are regularly used for surveillance, monitoring, and other routine tasks can benefit from this autonomy. Long haul deep sea transportation activities are additional opportunities. These two use cases present very different terrains—the first being coastal waters—with many obstacles, structures, and human presence while the latter is mostly devoid of such obstacles. Variations in environmental conditions are common to both terrains. Robust labeled datasets mapping such terrains are crucial in improving the situational awareness that can drive autonomy. However, there are only limited such maritime datasets available and these primarily consist of optical images. Although, long wave infrared (LWIR) is a strong complement to the optical spectrum that helps in extreme light conditions, a labeled public dataset with LWIR images does not currently exist. In this paper, we fill this gap by presenting a labeled dataset of over 2900 LWIR segmented images captured in coastal maritime environment over a period of 2 years. The images are labeled using instance segmentation and classified into seven categories—sky, water, obstacle, living obstacle, bridge, self, and background. We also evaluate this dataset across three deep learning architectures (UNet, PSPNet, DeepLabv3) and provide detailed analysis of its efficacy. While the dataset focuses on the coastal terrain, it can equally help deep sea use cases. Such terrain would have less traffic, and the classifier trained on cluttered environment would be able to handle sparse scenes effectively. We share this dataset with the research community with the hope that it spurs new scene understanding capabilities in the maritime environment. [ABSTRACT FROM AUTHOR]

Automatic road detection from remote sensing images is a vital application for traffic management, urban planning, and disaster management. The presence of occlusions like shadows of buildings, trees, and flyovers in high-resolution images and missclassifications in databases create obstacles in the road detection task. Therefore, an automatic road detection system is required to detect roads in the presence of occlusions. This paper presents a deep convolutional neural network to address the problem of road detection, consisting of an encoder-decoder architecture. The architecture contains a U-Network with residual blocks. UNetwork allows the transfer of low-level features to the high-level, helping the network to learn low-level details. Residual blocks help maintain the network's training performance, which may deteriorate due to a deep network. The encoder and decoder structures generate a feature map and classify pixels into road and non-road classes, respectively. Experimentation was performed on the Massachusetts road dataset. The results showed that the proposed model gave better accuracy than current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Use of the National Healthcare Safety Network (NHSN) has been essential to the success of the Massachusetts Hemovigilance Program and has allowed for the timely identification of signals and trends over a defined population that correlate with national and international hemovigilance (HV) data. Here, we outline how the NHSN system is used for monitoring HV data in Massachusetts and encourage adoption of NHSN for nationwide HV surveillance. Study Design and Methods: A collaboration that grew over time between local HV stakeholders and the Massachusetts Department of Public Health (MDPH) resulted in the change from a paper‐based method of reporting adverse reactions and monthly transfusion activity for compliance with state requirements to replacement with statewide adoption of reporting via NHSN. Results: Over 1.5 million blood products were transfused in Massachusetts between 2017 and 2021, with 3000 adverse reactions among 10 defined types reported. Using NHSN, MDPH has been able to produce numerous reports, publications, and presentations that have made previously non‐obtainable HV and blood utilization data available. Discussion: Although limitations to these self‐reported data exist, such as lack of external validation, successful statewide implementation of NHSN for hospital blood bank reporting is possible and has benefits beyond those for regulatory oversight. It results in standardized, actionable data at both the hospital and state level, enabling inter‐facility comparisons, benchmarking, and opportunities for practice improvement. [ABSTRACT FROM AUTHOR]

Efficient road and building footprint extraction from satellite images are predominant in many remote sensing applications. However, precise segmentation map extraction is quite challenging due to the diverse building structures camouflaged by trees, similar spectral responses between the roads and buildings, and occlusions by heterogeneous traffic over the roads. Existing convolutional neural network (CNN)-based methods focus on either enriched spatial semantics learning for the building extraction or the fine-grained road topology extraction. The profound semantic information loss due to the traditional pooling mechanisms in CNN generates fragmented and disconnected road maps and poorly segmented boundaries for the densely spaced small buildings in complex surroundings. In this paper, we propose a novel attention-aware segmentation framework, Multi-Scale Supervised Dilated Multiple-Path Attention Network (MSSDMPA-Net), equipped with two new modules Dynamic Attention Map Guided Index Pooling (DAMIP) and Dynamic Attention Map Guided Spatial and Channel Attention (DAMSCA) to precisely extract the building footprints and road maps from remotely sensed images. DAMIP mines the salient features by employing a novel index pooling mechanism to retain important geometric information. On the other hand, DAMSCA simultaneously extracts the multi-scale spatial and spectral features. Besides, using dilated convolution and multi-scale deep supervision in optimizing MSSDMPA-Net helps achieve stellar performance. Experimental results over the seven benchmark building and road extraction datasets namely, Porto, Shanghai, Massachusetts Road, Massachusetts Building, Synthinel-1, WHU Satellite I and WHU aerial Imagery dataset, ensures MSSDMPA-Net as the state-of-the-art (SOTA) method for building and road extraction as our method beats the next best alternatives by 5.94%, 2.55%, 3.97%, 11.64%, 6.86%, 6.92%, 2.57% IOU and 3.98%, 1.90%, 2.43%, 7.17%, 3.98%, 4.99%, 1.37% F1 score, respectively, over the mentioned datasets. The code has been released in: https://github.com/shirshabose/MSSDMPA-Net. • We propose a novel attention-guided pooling mechanism to alleviate information loss. • Prior generated probability map guided novel attention for multi-scale geometry-aware feature aggregation. • Multi-scale optimized with deep supervision to generate prior probability maps. • Dilated convolutions used to extract features of objects of varied shapes. • Multi-scale feature aggregator network for building and road segmentation. [ABSTRACT FROM AUTHOR]

Individual talkers vary in their relative use of different cues to signal phonological contrast. Previous work provides limited and conflicting data on whether such variation is modulated by cue trading or individual differences in speech style. This paper examines differential cue weighting patterns in Mandarin sibilants as a test case for these hypotheses. Standardized Mandarin exhibits a three-way place contrast between retroflex, alveopalatal, and alveolar sibilants with individual differences in relative weighting of spectral center of gravity (COG) and the second formant of the following vowel (F2). In results from a speech production task, cue weights of COG and F2 are inversely correlated across speakers, demonstrating a trade-off relationship in cue use. These findings are consistent with a cue trading account of individual differences in contrast signaling. [ABSTRACT FROM AUTHOR]

Greenways are important landscape ecology strategies. Landscape ecology and greenway planning were central to Julius Fábos's professional life and scholarship. This essay is a memorial to the late Julius Fábos (1932–2022), a Professor Emeritus at the University of Massachusetts, Amherst. It traces Julius early upbringings in Hungary before and during World War II, his academic career in the United States, and his contributions to the Iberian Greenway Planning movement. Although Julius Fábos's work was conducted primarily in Northeastern United States, it spread worldwide due to his unparalleled vision, leadership, and joie de vivre. Iberian landscape planning benefited greatly from Julius Fábos recurrent visits to Southern Europe, and indirectly from having students study with him at the University of Massachusetts, Amherst. After a thirty-five year teaching career, he launched a series of conferences aimed at disseminating knowledge on ecological thinking. The 7th Fábos Conference on Landscape and Greenway Planning took place 30 June–3 July 2022 in Budapest, Hungary. Julius Fábos's successful academic career offers many opportunities for inspiration and self-reflection, especially in the post-pandemic uncertain times of today. [ABSTRACT FROM AUTHOR]

As third-generation solar cells, dye-sensitized solar cells (DSSCs) can show bifacial harvesting capabilities by utilizing transparent conducting oxides as a counter electrode. Herein, the electrolyte is considered a critical layer from the optical perspective. In this paper, an attempt to estimate the optical properties of Iodine-based electrolytes, typically used in dye-sensitized solar cells, is demonstrated. The refractive index for electrolyte as an effective medium is calculated to be 1.4535 ± 0.005 for an effective thin film of 33.4 ± 0.5 μm thickness, using the near-infrared Fabry–Perot resonances. The extinction (absorption and scattering) and dispersion spectra for the prepared electrolyte were fitted using Lorentz–Dude (LD) model. Finally, the utility of the extracted optical parameters was examined through a finite difference time domain solver, Massachusetts Institute for Technology Electromagnetic Equation Propagation. The simulated optical transmission spectrum perfectly agreed with the measured spectrum with less than 0.1% root–mean–square error. The demonstrated attempt to accurately estimate the refractive index of electrolyte used in DSSCs fabrication may impacted theoreticians who are interested with the optoelectronic modelling of such electrochemical cells, as well as those dealing with optoelectronic devices informatics. [ABSTRACT FROM AUTHOR]

The Markforged Metal X (MfMX) printing machine (Markforged Inc., Massachusetts, USA) is one of the latest introduced additive manufacturing (AM) devices. It is getting popular because of its safety, simplicity, and ability to utilize various types of powders/filaments for printing. Despite this, only a few papers have so far reported the various properties and performances of the components fabricated by the MfMX printer. In this study, the microstructure and mechanical properties of MfMX-fabricated 17-4 stainless steel (ss) in the as-printed and heat-treated conditions were investigated. XRD and microscopy analyses revealed a dominant martensitic microstructure with some retained austenite phase. The microstructure is generally characterized by patterned voids that were unfilled due to a lack of fusion between the adjacent filaments. Disregarding these defects (voids), the porosity of the dense region was less than 4%. Depending on the heat treatment conditions, the hardness and tensile strength were enhanced by 17–28% and 21–27%, respectively. However, the tensile strength analyzed in this work was low compared with some previous reports for L-PBF-fabricated 17-4 ss. In contrast, the hardness of the as-printed (331 ± 28 HV) and heat-treated samples under the H900 condition (417 ± 29 HV) were comparable with (and even better than) some reports in the literature, despite the low material density. The results generally indicated that the Markforged printer is a promising technology when the printing processes are fully developed and optimized. [ABSTRACT FROM AUTHOR]

Background: The HEALing (Helping to End Addiction Long-termSM) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate-constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests.Methods: The primary outcome, the number of opioid overdose deaths, is count data assessed at the community level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the type I error rates and power for 3 tests: (1) Wald-type t-test with small-sample corrected empirical standard error estimates, (2) Wald-type z-test with model-based standard error estimates, and (3) permutation test with test statistics calculated by the difference in average residuals for the two groups.Results: Our simulation results demonstrated that Wald-type t-tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper type I error. Wald-type z-tests with model-based standard error estimates were anti-conservative. Permutation tests preserved type I error rates if the constrained space was not too small. For all tests, the power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA).Conclusions: Based on the results of our simulation study, the Wald-type t-test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study.Trial Registration: ClinicalTrials.gov http://www.Clinicaltrials: gov ; Identifier: NCT04111939. [ABSTRACT FROM AUTHOR]

This paper offers a critical analysis of program evaluation in contemporary urban agriculture. Drawing on data from an exploratory study designed at the request of and in collaboration with urban agriculture practitioners in Massachusetts, it describes both their critiques of extant practices of program evaluation and their visions for alternative ways of telling the story of their work. Related, it explores practitioners' interest in building capacity for policy advocacy, working collectively to create transformative social change, and, related, establishing new kinds of relationships with state and philanthropic funders. Building on scholarship that has observed that urban agriculture is characterized by an internal contradiction—i.e., its simultaneous orientation to "neoliberal" (social service) and "radical" (social justice) agendas (McClintock in Local Environ 19:147–171, 2014)—this analysis calls attention, especially, to the complex role of metrics, which may not only entrench neoliberalism in UA organizations, but also provide a mechanism for challenging its assumptions and advancing the radical project of food justice. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low building segmentation accuracy and blurred edges in high-resolution remote sensing images, an improved fully convolutional neural network is proposed based on the SegNet network. First, GELU, which performs well in deep learning tasks, is selected as the activation function to avoid neuron deactivation. Second, the improved residual bottleneck structure is used in the encoding network to extract more building features. Then, skip connections are used to fuse images The low-level and high-level semantic features are used to assist image reconstruction. Finally, an improved edge correction module is connected at the end of the decoding network to further correct the edge details of the building and improve the edge integrity of the building. Experiments are carried out on the Massachusetts building dataset, and the precision rate, recall rate, and F1 value reach 93.5%, 79.3%, and 81.9%, respectively, and the comprehensive evaluation index F1 value is improved by about 5% compared with the basic network. [ABSTRACT FROM AUTHOR]

School closings in response to COVID-19 reduced the opportunity for students to engage in all learning including career and technical education (CTE). As a result of the COVID-19 disruption student opportunities for work-based learning and completion of hours required for professional certification was severely reduced. The absence of these experiences had the potential to create disruptions in the skill and experience accumulation for youth and their transition to the labor market. Prior research demonstrated that CTE training and certifications in high school improve earnings and employment outcomes for students, particularly those from marginalized communities. This paper uses administrative data from Massachusetts to investigate whether COVID-19 disruptions differentially influenced outcomes for CTE students relative to non-CTE students. We find graduation rates fell for all students, but students in specialized CTE high schools experienced smaller declines. These smaller differences were apparent for CTE students from lower-income families and those with disabilities. In contrast, students in large cities where the influence of COVID-19 was elevated fared less well, even among CTE participants. In contrast to educational attainment, graduating seniors continued earning industry credentials comparably to pre-COVID-19 rates. This work illuminates how the pandemic disrupted youth transitions to adulthood, particularly in marginalized communities. [ABSTRACT FROM AUTHOR]

Transgender patients are at elevated risk of intimate partner violence (IPV), but national guidelines do not recommend routine screening for this population. This paper explores the feasibility and effectiveness of routine IPV screening of transgender patients in a primary care setting by describing an existing screening program and identifying factors associated with referral and engagement in IPV-related care for transgender patients. An IPV "referral cascade" was created for 1,947 transgender primary care patients at an urban community health center who were screened for IPV between January 1, 2014 to May 31, 2016: (a) Of those screening positive, how many were referred? (b) Of those referred, how many engaged in IPV-specific care within 3 months? Logistic regression identified demographic correlates of referral and engagement. Of the 1,947 transgender patients screened for IPV, 227 screened positive. 110/227 (48.5%) were referred to either internal or external IPV-related services. Of those referred to on-site services, 65/103 (63.1%) had an IPV-related appointment within 3 months of a positive screen. IPV referral was associated with being assigned male at birth (AMAB) versus assigned female at birth (AFAB) (AOR = 2.69, 95% CI 1.52, 4.75) and with nonbinary, rather than binary, gender identity (AOR = 2.07, 95%CI 1.09, 3.73). Engagement in IPV-related services was not associated with any measured demographic characteristics. Similar to published rates for cisgender women, half of transgender patients with positive IPV screens received referrals and two-thirds of those referred engaged in IPV-specific care. These findings support routine IPV screening and referral for transgender patients in primary care settings. Provider training should focus on how to ensure referrals are made for all transgender patients who screen positive for IPV, regardless of gender identity, to ensure the benefits of screening accrue equally for all patients. [ABSTRACT FROM AUTHOR]

Road segmentation is one of the essential tasks in remote sensing. Large-scale high-resolution remote sensing images originally have larger pixel sizes than natural images, while the existing models based on Transformer have the high computational cost of square complexity, leading to more extended model training and inference time. Inspired by the long text Transformer model, this paper proposes a novel hybrid attention mechanism to improve the inference speed of the model. By calculating several diagonals and random blocks of the attention matrix, hybrid attention achieves linear time complexity in the token sequence. Using the superposition of adjacent and random attention, hybrid attention introduces the inductive bias similar to convolutional neural networks (CNNs) and retains the ability to acquire long-distance dependence. In addition, the dense road segmentation result of remote sensing image still has the problem of insufficient continuity. However, multiscale feature representation is an effective means in the network based on CNNs. Inspired by this, we propose a multi-scale patch embedding module, which divides images by patches with different scales to obtain coarse-to-fine feature representations. Experiments on the Massachusetts dataset show that the proposed HA-RoadFormer could effectively preserve the integrity of the road segmentation results, achieving a higher Intersection over Union (IoU) 67.36% of road segmentation compared to other state-of-the-art (SOTA) methods. At the same time, the inference speed has also been greatly improved compared with other Transformer based models. [ABSTRACT FROM AUTHOR]

In 2012, the State of Massachusetts enacted a law known as the 'Right to Repair' requiring automakers to share vital vehicle information and data with consumers and small auto repair shops for maintenance and repair purposes. This paper assesses the impact of this law on the number and percentage of small repair shops in the state. The results of a synthetic control and a difference-in-differences analysis show that the law led to increases in the number and market share of small auto repair shops. [ABSTRACT FROM AUTHOR]

Objectives: Dental fear and anxiety are known determinants of delaying or avoiding dental care and vary considerably based on factors such as age and gender. However, little is known about dental fear and anxiety in racial/ethnic minority populations, which bear a disproportionate burden of poor oral health outcomes. Structural and social pathways responsible for producing these disparities are also understudied. Experiences of racism over the lifecourse may contribute to poor oral health outcomes through a pathway of dental fear and anxiety. This paper aimed to evaluate perceived experiences with racism, dental fear and anxiety, and the utilization of dental services, in the Black Women's Health Study (BWHS), a United States‐based prospective cohort. Methods: Analysis of prospective data obtained from a geographic subset of participants in the BWHS was conducted. In 2014, BWHS participants residing in Massachusetts responded to a mailed oral health questionnaire that included the Index of Dental Anxiety and Fear (IDAF‐4C+) instrument (N = 484; 69% response rate). Previously collected demographic and health information, along with reported experiences of everyday and lifetime racism, obtained from national BWHS questionnaires between 1995 and 2009, were merged with the Massachusetts‐based sub‐sample. Associations between high dental anxiety (HDA) (mean IDAF‐4C+ score ≥2.5 on the dental fear and anxiety module) and oral health outcomes and perceived racism and HDA were explored via prevalence ratios (PR) calculated using log‐binomial regression models, including adjustment for potential confounders. Results: Reported exposures to everyday racism occurred weekly on average for the top 25% of the sample, while 13% of participants reported exposure to multiple (n = 3) experiences of unfair treatment due to their race over their lifetime. HDA was prevalent among 17.8% of the sample and was significantly associated with indicators of poor oral health status. High exposures to everyday and lifetime experiences of racism were positively associated with HDA (PR = 1.08; 95% CI: 0.90, 1.58 and PR = 1.72; 95% CI: 1.03, 2.88, respectively). Conclusions: Significant associations between racism and HDA, and between HDA and poor oral health and reduced utilization of dental care were observed. Dental anxiety may be a pathway through which perceived experiences with racism may impact oral health outcomes. [ABSTRACT FROM AUTHOR]

Healthcare policy makers use wait-time metrics to encourage hospital managers to improve patient experience. In 2002, Massachusetts mandated that hospital managers develop processes to respond to boarding crises, which occur when emergency department (ED) patients experience long waits for inpatient beds. Performance improvement theory suggests that patients would be better served by preventing boarding crises rather than responding urgently after they occur. To empirically test this theory, we use data from a Massachusetts hospital that has two physician-based processes related to boarding and patient flow. First, to comply with the state mandate, the hospital developed processes to identify when the hospital is in a boarding crisis, a code yellow (CY), and subsequently request that physicians prioritize patient discharge (urgent response). Second, physicians can use predischarge orders, optional written communication about discharge barriers, to avoid discharge delays for patients approaching discharge (prevention response). Our data supports the existence of a trade-off between these two responses. Counter to our hypothesis, the state-mandated urgent response does not have any impact on length of stay (LOS). We also find that a CY has no impact on ED hourly occupancy, marginally decreases ED wait times, and increases boarding time. The prevention response is associated with a 26% reduction in LOS. Furthermore, we find that the urgent response reduces the likelihood of physicians' ability to use the prevention response by 27.3%. We conclude that the state policy has unintended negative consequences that stymie hospital efforts to create longer term improvement. This paper was accepted by Stefan Scholtes, healthcare management. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4724. [ABSTRACT FROM AUTHOR]

Accurately segmenting roads is challenging due to substantial intra-class variations, indistinct inter-class distinctions, and occlusions caused by shadows, trees, and buildings. To address these challenges, it is crucial to simultaneously pay attention to important texture details and perceive global geometric context information. Recent research has shown that CNN-Transformer hybrid structures outperform using CNN or Transformer alone. CNN excels at extracting local detail features and Transformer naturally has the ability to perceive global context information. In this paper, we propose a dual-branch network module called ConSwin, which combines the advantages of ResNet and Swin Transformer for better road information extraction. Taking ConSwin as the basic encoding module, we build an hourglass-shaped network with an encoder-decoder structure. In order to better transfer texture and structural detail information between the encoder and decoder, we also design two novel connection blocks. We conduct comparative experiments on the Massachusetts and CHN6-CUG datasets, and the proposed method outperforms state-of-the-art methods in terms of overall accuracy, IOU, and F1 metrics. Other experiments verify the effectiveness of our method, while visualization results demonstrate its ability to obtain better road representations. [ABSTRACT FROM AUTHOR]

This paper examines the role of universal healthcare on patient choice and quality of care. Lack of health insurance is a leading contributor to emergency department (ED) visits from the uninsured. Using a natural experiment from Massachusetts, we follow previously uninsured individuals over time—both before and after the implementation of universal healthcare—to study the effects of insurance availability on their healthcare seeking behavior. We find that the availability of health insurance has a significant impact on the type of healthcare sought by the previously uninsured. Specifically, individuals are less likely to continue visiting the ED and instead more likely to choose hospital inpatient and outpatient services. We find that frequent fliers and patients with chronic conditions, who are both known to account for a substantial share of healthcare costs, exhibit heterogeneity in this behavior. Finally, we examine the effect of the policy on quality of care, as measured by short‐term ED revisits. Although the overall revisit rates remain unchanged, frequent ED users and chronic patients experience a decline in short‐term ED revisits. With key features of the Affordable Care Act being frequently criticized for repeal, understanding how these changes influence patient choice and quality of care is significantly policy relevant. [ABSTRACT FROM AUTHOR]

Background: Despite the importance of family and parent-focused practice, there has been a dearth of research on interventions for parents with mental illness. This paper describes the process and outcome of adapting an evidence-based intervention, Let's Talk about Children (LTC), in the context of adult mental health services in Massachusetts, United States. Methods: Specific objectives included: (1) to specify the core components, functions, and principles of LTC essential to adapting the intervention (i.e., program theory), (2) to consider contextual factors related to the new setting; (3) to pre-test the adapted materials with diverse practitioners; and (4) to compile the program model and materials (i.e., the practice profile) for use by adult mental health service providers in Massachusetts. The Adaptation Team included individuals with expertise in psychiatric rehabilitation and clinical care, policymaking, program development and research, and parents. Activities occurred between 2015–2019 and included: (1) consulting with experts to specify the core elements and theory behind the selected intervention (i.e., with the LTC purveyor and international experts); (2) consulting with key stakeholders for input regarding the Massachusetts target population and context to inform adaptations (i.e., individual and group key informant interview sessions); (3) pretesting the initial adapted materials (i.e., training and coaching sessions with adult mental health practitioners); and (4) using feedback to refine and compile the final intervention manual (i.e., the ParentingWell Practice Profile). Participants reflected diverse, oftentimes multiple roles and perspectives, including those of parents with mental illness, adult children, and family members. Results: ParentingWell is practitioner- and setting-agnostic, addresses parenting across the lifespan, fits into the routine workflow, and builds on practitioners' existing skills. Eight themes emerged, which were translated into four core elements (engage, explore, plan, access and advocate) consistent with Self-Determination Theory and four underlying principles (trauma-informed, strengths-based, family-focused, culturally sensitive) in keeping with the LTC model. The ParentingWell Practice Profile operationalizes each core element and addresses the underlying principles. Conclusion: ParentingWell makes talking about parenting and family experiences a routine part of the therapeutic conversation with adults with mental illness. Future research will test the adaptation, implementation, and impact of ParentingWell. [ABSTRACT FROM AUTHOR]

Hospital and healthcare administrators name high prescription drug costs as one of their largest problems. A significant body of research demonstrates that meals and honoraria from pharmaceutical firms to physicians leads to higher prescribing of expensive, brand name drugs, despite little difference in efficacy. Some administrators and scholars have advocated for mandatory disclosure of these payments in order to reduce this conflict of interest, but many practitioners believe disclosure has little effect on prescribing, and the empirical evidence is mixed. This paper uses a quasi-experiment of a 2009 payment disclosure policy in Massachusetts to estimate the causal impact of public disclosure on prescribing. The comprehensive data set includes all retail prescriptions for 262 drugs in nine drug classes written by 5,730 physicians in five states over 48 months. We show a significant postdisclosure reduction in brand name drug prescriptions by Massachusetts physicians, relative to control physicians in other states. These effects are driven by heavy prescribers of brand name drugs in the prepolicy period, particularly for drugs with large prepolicy sales forces. Effects are also detected before the first data were released, implying that the effects are not because patients or administrators responded to the disclosed payments. Instead, some physicians may have changed their payments and prescriptions behavior to avoid appearing biased. Taken in tandem with the many studies showing that pharmaceutical industry payments influence prescribing, this study suggests a strong role for mandatory public disclosure in reducing conflicts of interest in medicine and costly prescribing of brand name drugs. This paper was accepted by Stefan Scholtes, healthcare management. [ABSTRACT FROM AUTHOR]

Although key stakeholders have discussed responsible gambling tools and protective behavioral strategies for years, evaluations of their effectiveness are still limited. Among protective behavioral strategies are individual gambling thresholds, typically on monetary losses or time spent gambling, after which a person stops gambling. A novel, counterintuitive alternative, a monetary win threshold, also might hold value. Simulations have shown that, like monetary loss or time thresholds, win thresholds reduce the amount of time spent gambling and therefore also limit average expected gambling losses. Yet, little is known about gamblers' use of gambling thresholds. In this paper, we examine data from an Internet panel survey of past-year gamblers in Massachusetts to better understand the characteristics of those individuals who are more likely to use and adhere to loss and win thresholds. We observed that individuals who had engaged in recreational drug use were less likely to adopt gambling thresholds. Individuals who had previously received a positive screen for depression, and who traveled to out-of-state casinos were more likely to use gambling thresholds. In analyzing the adherence to gambling thresholds, we found that individuals who adhered to their loss thresholds were less likely to use ATMs during gambling sessions. Finally, individuals who engaged in hazardous drinking were less likely to adhere to their own win thresholds. This study adds to the literature by providing evidence related to the characteristics of gambling threshold users and contributes some of the only evidence in the literature on the actual use of monetary win thresholds. [ABSTRACT FROM AUTHOR]

The term "prevention paradox" focuses on the notion that more aggregate harm is experienced by low-risk individuals even though high-risk individuals experience the greatest amount of harm per individual. This paper examines whether the prevention paradox in relation to gambling harms exists in Massachusetts. The analysis is drawn from two population surveys and the distribution of harms across four gambling severity groups is examined. The results show that because of the larger size of the three lower severity groups, even the much smaller average number of harms endorsed by members of these groups accounts for nearly three-quarters (72.9%) of the aggregate number of harms across all groups. While almost all individuals in the highest severity group report one or more harms, any individual reporting one or more harms is more likely to be in a lower severity group. Financial, health, and emotional/psychological harms are more common and more broadly distributed across the gambling severity groups compared to relationship, work/school, and illegal harms. In contrast to a similar study in Finland, which found that the most severe group accounted for over 50% of the harms in the health, relationship, and illegal harm domains, the prevention paradox is supported across all harm domains in Massachusetts. [ABSTRACT FROM AUTHOR]

The authors reflect on the past achievements, disappointments, failures, and successes of the journal after assuming the position of Editors-in-Chief in 2023. They talk about the intense process of learning and analysis they have gone through over the past 12 months, their commitment to strengthening the journal's reputation and expanding its reach, and article types they are planning to include to broaden the scope, and impact of the journal.

Background/Purpose: A growing body of evidence suggests that people who are arrested for driving under the influence (DUI) of alcohol are more likely to screen positive for psychiatric disorders than the general population. Additionally, psychiatric comorbidity has been shown to increase vulnerability to criminal re-offense. However, DUI programs face many barriers to screening for psychiatric disorders. This paper evaluates the sensitivity and specificity of a screening tool developed for these programs, the Computerized Assessment and Referral System (CARS) Screener. Methods: We used data from 381 DUI offenders in Massachusetts, as well as a secondary data source, the National Comorbidity Survey-Replication (NCS-R: N = 9,282) to examine the accuracy of the CARS Screener when compared to full assessment. Results: Based on both sets of analyses, we found that the CARS Screener offers a sensitive and specific method to screen for many psychiatric disorders. Specifically, the CARS Screener has a high sensitivity and specificity for bipolar disorder, intermittent explosive disorder, depressive disorders, generalized anxiety disorder, alcohol and drug use disorders, gambling disorder, post-traumatic stress disorder, panic attacks, and social phobia. Conclusion: The CARS Screener appears to be an effective tool that will help DUI programs better understand and address the mental health issues facing their clients. [ABSTRACT FROM AUTHOR]

A formal semantic analysis of the tough-construction is provided building on the well-known observation that events play a central role. A close look at the semantic characteristics of the class of tough-predicates and the syntactic and semantic properties of nonfinite clauses reveals the link between these pieces, expanding on recent advances in the semantics of clauses (Moulton in Natural selection and the syntax of clausal complementation, PhD thesis, University of Massachusetts at Amherst, 2009). Building on Salzmann (Reconstruction and resumption in indirect A′-dependencies: On the syntax of prolepsis and relativization in (Swiss) German and beyond, de Gruyter, Berlin 2017b), a formal semantic and syntactic analysis of prolepsis is provided to explain the antecedent gap chain in the tough-construction. In total, this paper offers a description and explanation of (i) the class of tough-predicates; (ii) the properties of the nonfinite clause that appears in the tough-construction; (iii) why no other predicates or clauses are permitted in the construction; (iv) the many properties of the antecedent gap chain in the tough-construction, primarily the fact that the chain is "weakly" unbounded; and (v) the semantic contribution of prolepsis as it applies in the tough-construction. [ABSTRACT FROM AUTHOR]

While scholars have found that undergraduate involvement in research is beneficial, the lack of such experiences in the social sciences and humanities is glaring. This paper analyzes how an emphasis on community through cohort models impacts undergraduate student experience in research, taking from the Emerging Scholars Program, an interdisciplinary research program where cohorts of undergraduates are matched with faculty and attend meetings, workshops, and presentations together. We find that the cohort model created a robust community that fosters positive relationships that develop professional skills and emotional support, allowing students to collaborate and aim more toward their professional goals. The results from this study offer valuable insights into how universities can ensure that students have meaningful research experience beyond the classroom.