Maryland Shared Open Access Repository

MD-SOAR is a shared digital repository platform for twelve colleges and universities in Maryland. It is currently funded by the University System of Maryland and Affiliated Institutions (USMAI) Library Consortium (usmai.org) and other participating partner institutions. MD-SOAR is jointly governed by all participating libraries, who have agreed to share policies and practices that are necessary and appropriate for the shared platform. Within this broad framework, each library provides customized repository services and collections that meet local institutional needs. Please follow the links below to learn more about each library's repository services and collections.

 

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How do we respond? The examination of Howard University's response to the 2021 #BlackburnTakeover
(2024-02-20) Simmons, Melissa M.; Guha, Pallavi; Towson University. Communication Management Program
Higher education administrators face several challenging tasks during their tenure at an institution—and managing student protests is one of the most daunting. Many student activists select radical approaches to demand change from administrators. Occupying campus buildings, crashing donor lunches, interrupting admissions tours, and contacting media outlets are just a few tactics. Administrators must swiftly and effectively communicate with students and other stakeholders to reinforce their care for students' concerns and restore trust with their community. If not, the institution's reputation, daily business operations, and enrollment and philanthropic efforts are on the line. With all that is at stake, one of the most critical questions administrators ask themselves during a protest is, "How should we respond?" This study focused on higher education administrators’ discourse during the Blackburn Takeover at Howard University. Data included Twitter posts from the beginning up to the announcement of the end of the takeover. Framing devices, situational crisis communication theory (SCCT) strategies, and other themes were examined. Thematic analysis software was used to determine whether responses to discourse were positive, negative, or indifferent.
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CARE COMPARE STAR RATINGS AND FAMILY SATISFACTION IN MARYLAND NURSING FACILITIES
(Oxford University Press, 2023-12-21) Millar, Roberto; Diehl, Christin; Kusmaul, Nancy; Stockwell, Ian
Nursing facilities provide critical services and supports to individuals with long-term care needs. The quality of care in nursing facilities varies depending on facility structural characteristics. Moreover, the measurement and perceptions of what constitutes quality of care varies across stakeholders. We used publicly available data to examine the association between family satisfaction with care and the Centers for Medicare & Medicaid Services’(CMS’s) Care Compare five-star quality ratings in the context of facility characteristics. Facility-level data of family satisfaction with care were merged with CMS’s five-star star ratings of 220 Maryland nursing facilities in 2021. Using univariate and bivariate statistics, we explored differences in family ratings and five-star ratings across facility ownership (for-profit vs. non-profit), geographic location (urban vs. rural), and average resident occupancy (1-60, 61-120, and 121+). Relationships were examined across overall ratings, as well as across subdomain of the two quality rating frameworks (e.g., staffing, autonomy, health inspections). Family members of residents in non-profit, rural, and low-occupancy facilities rated facilities higher. Non-profit and low-occupancy facilities were statistically more likely to be rated four or five stars, while no significant association was observed across geographic location. The association between subdomain-specific family satisfaction and star ratings varied across facilities of different structures. Findings emphasize the need for comprehensive quality of care frameworks that consider views of quality across stakeholders and types of facilities. A clear understanding of nursing facility structure and quality of care is critical to advance data-driven decision making.
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Designing Optimal Recommended Budgeting Thresholds for a Medicaid Program
(AJMC, 2022-07-14) Henderson, Morgan; Stockwell, Ian
Objectives: To develop and test a methodology for optimally setting automatic auditing thresholds to minimize administrative costs without encouraging overall budget growth in a state Medicaid program. Study Design: Two-stage optimization using administrative Maryland Medicaid plan-of-service data from fiscal year (FY) 2019. Methods: In the first stage, we use an unsupervised machine learning method to regroup acuity levels so that plans of service with similar spending profiles are grouped together. Then, using these regroupings, we employ numerical optimization to estimate the recommended budget levels that could minimize the number of audits across those groupings. We simulate the effects of this proposed methodology on FY 2019 plans of service and compare the resulting number of simulated audits with actual experience. Results: Using optimal regrouping and numerical optimization, this method could reduce the number of audits by 10.4% to 36.7% relative to the status quo, depending on the search space parameters. This reduction is a result of resetting recommended budget levels across acuity groupings, with no anticipated increase in the total recommended budget amount across plans of service. These reductions are driven, in general, by an increase in recommended budget level for acuity groupings with low variance in plan-of-service spending and a reduction in recommended budget level for acuity groupings with high variance in plan-of-service spending. Conclusions: Using machine learning and optimization methods, it is possible to design recommended budget thresholds that could lead to significant reductions in administrative burden without encouraging overall cost growth.
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Relationship between Event Prevalence Rate and Gini Coefficient of Predictive Model
(Canadian Center of Science and Education, 2022-02) Han, Fei; Stockwell, Ian
Predictive models are currently used for early intervention to help identify patients with a high risk of adverse events. Assessing the accuracy of such models is a crucial part of the development process. To measure the predictive performance of a scoring model, quantitative indices such as the K-S statistic and C-statistic are used. This paper discusses the relationship between Gini coefficients and event prevalence rates. The main contribution of the paper is the theoretical proof of the relationship between the Gini coefficient and event prevalence rate.
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A Landscape-Based Ecological Classification System For River Valley Segments in Lower Michigan (MI-VSEC Version 1.0)
(MICHIGAN DEPARTMENT OF NATURAL RESOURCES, 1997-12-31) Seelbach, Paul W.; Wiley, Michael J.; Kotanchik , Jennifer C.; Baker, Matthew
Through ecological classification, researchers both (1) identify and (2) describe naturally-occurring, ecologically-distinct, spatial units from a holistic perspective. An ecological river classification involves the identification of structurally homogeneous spatial units which emerge along the channel network as a result of catchment processes interacting with local physiographic features. Our observations of Michigan rivers suggest that the natural ecological unit, as defined by the spatial scales of riverine physical and biological processes, is most closely approximated by the physical channel unit termed the valley segment. Valley segments are generally quite large, and characterized by relative homogeneity in hydrologic, limnologic, channel morphology, and riparian dynamics. Valley segment characteristics often change sharply at stream junctions, slope breaks, and boundaries of local landforms. We followed several steps in developing an ecological classification for the rivers of lower Michigan. Step 1 – We first selected catchment size, hydrology, water chemistry, water temperature, valley character, channel character, and fish assemblages as fundamental attributes to describe ecological character of river valley segments. Steps 2-3 – Two experienced aquatic ecologists worked together, interpreting map information on catchment and valley characteristics from a GIS, using their combined knowledge of ecological processes and interactions. We initially examined several key maps to become familiar with the general landscape patterns of a particular catchment; and to then identify initial valley segment units as defined by catchment and valley characteristics, and fish assemblages. Boundary definition required the integration of terrain features observed on several thematic maps (e.g., major stream network junctions, slope breaks, boundaries of major physiographic units or land cover units; or changes in stream sinuousity and meander wavelength patterns, riparian wetlands, or valley shape), combined with knowledge of fish distributions. We next developed categorizations for each component attribute and assigned category values for attributes to each segment unit. Assignments were based on map-interpretation rules drawn from modeling, survey data, and field experiences. Step 4 – our results were stored as a map and a table in ArcView 3.0 format. In all, we partitioned and classified the 19 largest river systems in lower Michigan. Summaries of the attributes assigned to over 270 river valley segments (covering mainstems and major tributaries) provided an initial description of the river resources of lower Michigan. Managers of lower Michigan rivers will be able to develop many of their thoughts and activities within this framework of ecological units. Development of this system is intended to be ongoing; with the extension of coverage to upper Michigan, the continued validation of attribute codings, and the addition of new attributes.