Chairs Checklist for Shared Decision Making

UMass ADVANCE survey results indicate that women faculty are less clear on personnel processes than men, and women faculty members from underrepresented racial minority groups are the least clear on tenure and promotion processes. This checklist is for department chairs/heads to assess departmental shared decision-making and create equitable practices that will support the inclusion and retention of women faculty and faculty of color

Setting the Stage for Equitable Faculty Shared Decision-Making

The most consequential decisions regarding faculty careers are decided collectively by peers, including tenure, promotions, and annual merit pay raises. The shared decision making inherent in faculty governance is a type of collaboration that faculty experience in unequal ways. While most departments have formal procedures and written policies, academia remains dominated by informal ways of functioning that allow gender and racial inequalities to persist. UMass ADVANCE survey results indicate that women faculty are less clear on personnel processes than men, and women faculty members from underrepresented racial minority groups are the least clear on tenure and promotion processes. Women faculty are often uncertain about their next career steps. Creating equitable practices around shared decision-making will improve transparency and trust among colleagues, supporting the inclusion and retention of women faculty and faculty of color, especially those at junior ranks. How can governance be reinvented to be more equitable for women faculty and faculty of color

Preventing dentures and putting aside the fry bread: A systematic review of micro, mezzo, and macro conditions for dental health and obesity interventions for Native American youth

This systematic literature review was focused on childhood obesity and dental health interventions which have relevance to Native American communities. Childhood oral health and obesity have become significant problems across North America and among Nati

Rethinking the Core List of Journals for Libraries that Serve Schools and Colleges of Pharmacy.

The Core List of Journals for Libraries that Serve Schools and Colleges of Pharmacy is a guide for developing and maintaining pharmacy-affiliated library collections. A work group was created to update the list and design a process for updating that will streamline future revisions. Work group members searched the National Library of Medicine catalog for an initial list of journals and then applied inclusion criteria to narrow the list. The work group finalized the fifth edition of the list with 225 diverse publications and produced a sustainable set of criteria for journal inclusion, providing a structured, objective process for future updates

Successful Strategies for Discharging Medicaid Nursing Home Residents with Mental Health Diagnoses to the Community

The state and federal push to transition Medicaid residents from nursing homes to the community calls for effective discharge strategies targeted to residents’ diverse needs. This exploratory, mixed-methods study utilized the Minimum Data Set to describe demographics, health characteristics, and transition patterns of Kansas Medicaid residents with mental health diagnoses who were discharged from nursing homes from 2005 to 2008. Discharged residents (n = 720) had multiple comorbidities, and more than half remained in the community following their first nursing home event. In-depth interviews with nursing home staff (n = 11) explored successful discharge strategies. Successful strategies support an ecological approach to meeting individual, family, organizational, and community needs. This includes creating/sustaining a culture of discharge, encompassing informal and formal community supports in the discharge process, proactively addressing physical environment needs, and assisting individuals and their family members in managing physical and mental health conditions. Findings suggest that policies in the areas of preadmission screening, caregiver support, and revised Medicaid reimbursement are needed to better support continuity of care and promote discharge for nursing home residents with complex care needs. Future research could examine individual and family perspectives on the discharge process and track outcomes when transitioning between settings

Microbial exposures in infancy predict levels of the immunoregulatory cytokine interleukin-4 in filipino young adults

Infancy represents a window of development during which long-term immunological functioning can be influenced. In this study, we evaluate proxies of microbial exposures in infancy as predictors of interleukin-4 (IL-4) in young adulthood. Interleukin-4 (IL-4) is an immunoregulatory cytokine that plays a role in the pathogenesis of atopic and allergic disease

An Arabic Version of the Spiritual Well-Being Scale

This article reports on two studies to develop and validate an Arabic language version of the Spiritual Well-Being Scale (SWBS). The first study was a pilot study at a major government university in Jordan (N = 75, students). The second and main study was conducted in 5 large regional hospitals in Jordan (N = 63, patients). The SWBS was translated from English to Arabic and reviewed by an expert panel for language, cultural, and spiritual consistency. The Arabic version of the SWBS was revised after the results of the pilot study and further reviewed by an expert panel. The resulting data were subjected to descriptive and factor analysis. Results showed that the final version of the SWBS used in the main study had a two-factor structure consistent with previous studies. Descriptive data for a range of demographic variables are presented. Issues of inadequate translation and lack of variation in responses for some items are identified and the results discussed in light of dominant Islamic theological frameworks. © 2012 Taylor and Francis Group, LLC

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models.Random forests (RF) using covariates selected via recursive feature elimination was consistently most accurate, or was among the most accurate, classifiers sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. 43 Individual subgroup class accuracy was generally dependent upon the number of soil pedon 44 observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil-landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area

The politicisation of science in the Murray-Darling Basin, Australia:discussion of ‘Scientific integrity, public policy and water governance’

Many water scientists aim for their work to inform water policy and management, and in pursuit of this objective, they often work alongside government water agencies to ensure their research is relevant, timely and communicated effectively. A paper in this issue, examining 'Science integrity, public policy and water governance in the Murray-Darling Basin, Australia’, suggests that a large group of scientists, who work on water management in the Murray-Darling Basin (MDB) including the Basin Plan, have been subject to possible ‘administrative capture'. Specifically, it is suggested that they have advocated for policies favoured by government agencies with the objective of gaining personal benefit, such as increased research funding. We examine evidence for this claim and conclude that it is not justified. The efforts of scientists working alongside government water agencies appear to have been misinterpreted as possible administrative capture. Although unsubstantiated, this claim does indicate that the science used in basin water planning is increasingly caught up in the politics of water management. We suggest actions to improve science-policy engagement in basin planning, to promote constructive debate over contested views and avoid the over-politicisation of basin science

A Multi-Site Randomized Trial of a Clinical Decision Support Intervention to Improve Problem List Completeness

OBJECTIVE: To improve problem list documentation and care quality. MATERIALS AND METHODS: We developed algorithms to infer clinical problems a patient has that are not recorded on the coded problem list using structured data in the electronic health record (EHR) for 12 clinically significant heart, lung, and blood diseases. We also developed a clinical decision support (CDS) intervention which suggests adding missing problems to the problem list. We evaluated the intervention at 4 diverse healthcare systems using 3 different EHRs in a randomized trial using 3 predetermined outcome measures: alert acceptance, problem addition, and National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) clinical quality measures. RESULTS: There were 288 832 opportunities to add a problem in the intervention arm and the problem was added 63 777 times (acceptance rate 22.1%). The intervention arm had 4.6 times as many problems added as the control arm. There were no significant differences in any of the clinical quality measures. DISCUSSION: The CDS intervention was highly effective at improving problem list completeness. However, the improvement in problem list utilization was not associated with improvement in the quality measures. The lack of effect on quality measures suggests that problem list documentation is not directly associated with improvements in quality measured by National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) quality measures. However, improved problem list accuracy has other benefits, including clinical care, patient comprehension of health conditions, accurate CDS and population health, and for research. CONCLUSION: An EHR-embedded CDS intervention was effective at improving problem list completeness but was not associated with improvement in quality measures