A Scalable Model for Monograph Assessment: A Case Study at Musselman Library

The evolution of monograph assessment in Musselman Library resulted in a model that sustains concurrent assessment initiatives large and small, as well as time-bound and ongoing, with the purpose of shaping collections in support of the academic and creative interests at Gettysburg College. This presentation outlines the design of the 2012 assessment model that has become the foundation for assessing our circulating monograph collection, along with how the original model has been adjusted to assess more focused targets and larger initiatives, each with rapidly approaching deadlines. Finally, this presentation summarizes the workflows needed to support continuous decision making and provides a sample of the results from the assessment initiatives described

LGBTQ & You: Connecting Collections with the Campus Community

Musselman Library’s LGBTQ Research Guide, established in 2012, is a resource that goes beyond connecting the library’s collections with the campus community and providing access. This research guide has generated opportunities to grow campus partnerships, foster a student’s interest in librarianship, and create a gateway for research and learning in the LGBTQ community that goes beyond the classroom. In our presentation we will outline the project from its early days as a student project to its current life as collaboration between the library and Gettysburg Colleges’ Office of LGBTQA Advocacy & Education

Using Radical Adult Education to Map Change in a Globalized World

Radical adult education using a sociological frame can support adult educators to see their roles as change agents within their spheres of influence. Using cultural mapping, adult educators define these spheres, stake claims, set benchmarks, grow networks, or develop participatory action research within the identified community

New England StatNet: A Community of Practice in Performance Measurement

One way public organizations improve services is through the implementation of performance measurement programs. To help public managers address the challenges of performance measurement, knowing how others are using data in decision-making is valuable. Communities of practice are one way for public managers to access such information. StatNet began in 2008 as a network of municipal officials using data-driven performance management approaches. The group gathers three times per year for in-depth discussion of municipal governance, focusing on topics such as police, fire, budgets, constituent relations and DPWs

Incidence and Outcomes Associated With Clostridium difficile Infections: A Systematic Review and Meta-analysis

Importance: An understanding of the incidence and outcomes of Clostridium difficile infection (CDI) in the United States can inform investments in prevention and treatment interventions. Objective: To quantify the incidence of CDI and its associated hospital length of stay (LOS) in the United States using a systematic literature review and meta-analysis. Data Sources: MEDLINE via Ovid, Cochrane Library Databases via Wiley, Cumulative Index of Nursing and Allied Health Complete via EBSCO Information Services, Scopus, and Web of Science were searched for studies published in the United States between 2000 and 2019 that evaluated CDI and its associated LOS. Study Selection: Incidence data were collected only from multicenter studies that had at least 5 sites. The LOS studies were included only if they assessed postinfection LOS or used methods accounting for time to infection using a multistate model or compared propensity score-matched patients with CDI with control patients without CDI. Long-term-care facility studies were excluded. Of the 119 full-text articles, 86 studies (72.3%) met the selection criteria. Data Extraction and Synthesis: Two independent reviewers performed the data abstraction and quality assessment. Incidence data were pooled only when the denominators used the same units (eg, patient-days). These data were pooled by summing the number of hospital-onset CDI incident cases and the denominators across studies. Random-effects models were used to obtain pooled mean differences. Heterogeneity was assessed using the I2 value. Data analysis was performed in February 2019. Main Outcomes and Measures: Incidence of CDI and CDI-associated hospital LOS in the United States. Results: When the 13 studies that evaluated incidence data in patient-days due to hospital-onset CDI were pooled, the CDI incidence rate was 8.3 cases per 10 000 patient-days. Among propensity score-matched studies (16 of 20 studies), the CDI-associated mean difference in LOS (in days) between patients with and without CDI varied from 3.0 days (95% CI, 1.44-4.63 days) to 21.6 days (95% CI, 19.29-23.90 days). Conclusions and Relevance: Pooled estimates from currently available literature suggest that CDI is associated with a large burden on the health care system. However, these estimates should be interpreted with caution because higher-quality studies should be completed to guide future evaluations of CDI prevention and treatment interventions

Incidence and Outcomes Associated With Infections Caused by Vancomycin-Resistant Enterococci in the United States: Systematic Literature Review and Meta-Analysis

Information about the health and economic impact of infections caused by vancomycin-resistant enterococci (VRE) can inform investments in infection prevention and development of novel therapeutics. To systematically review the incidence of VRE infection in the United States and the clinical and economic outcomes. We searched various databases for US studies published from January 1, 2000, through June 8, 2015, that evaluated incidence, mortality, length of stay, discharge to a long-term care facility, readmission, recurrence, or costs attributable to VRE infections. We included multicenter studies that evaluated incidence and single-center and multicenter studies that evaluated outcomes. We kept studies that did not have a denominator or uninfected controls only if they assessed postinfection length of stay, costs, or recurrence. We performed meta-analysis to pool the mortality data. Five studies provided incidence data and 13 studies evaluated outcomes or costs. The incidence of VRE infections increased in Atlanta and Detroit but did not increase in national samples. Compared with uninfected controls, VRE infection was associated with increased mortality (pooled odds ratio, 2.55), longer length of stay (3-4.6 days longer or 1.4 times longer), increased risk of discharge to a long-term care facility (2.8- to 6.5-fold) or readmission (2.9-fold), and higher costs ($9,949 higher or 1.6-fold more). VRE infection is associated with large attributable burdens, including excess mortality, prolonged in-hospital stay, and increased treatment costs. Multicenter studies that use suitable controls and adjust for time at risk or confounders are needed to estimate the burden of VRE infections

Predicting medication adherence using ensemble learning and deep learning models with large scale healthcare data

Clinical studies from WHO have demonstrated that only 50-70% of patients adhere properly to prescribed drug therapy. Such adherence failure can impact therapeutic efficacy for the patients in question and compromises data quality around the population-level efficacy of the drug for the indications targeted. In this study, we applied various ensemble learning and deep learning models to predict medication adherence among patients. Our contribution to this endeavour involves targeting the problem of adherence prediction for a particularly challenging class of patients who self-administer injectable medication at home. Our prediction pipeline, based on event history, comprises a connected sharps bin which aims to help patients better manage their condition and improve outcomes. In other words, the efficiency of interventions can be significantly improved by prioritizing the patients who are most likely to be non-adherent. The collected data comprising a rich event feature set may be exploited for the purposes of predicting the status of the next adherence state for individual patients. This paper reports on how this concept can be realized through an investigation using a wide range of ensemble learning and deep learning models on a real-world dataset collected from such a system. The dataset investigated comprises 342,174 historic injection disposal records collected over the course of more than 5 years. A comprehensive comparison of different models is given in this paper. Moreover, we demonstrate that the selected best performer, long short-term memory (LSTM), generalizes well by deploying it in a true future testing dataset. The proposed end-to-end pipeline is capable of predicting patient failure in adhering to their therapeutic regimen with 77.35% accuracy (Specificity: 78.28%, Sensitivity: 76.42%, Precision: 77.87%, F1 score: 0.7714, ROC AUC: 0.8390)

Preparation, structured deliberate practice and decision making in elite level football: The case study of Gary Neville (Manchester United FC and England)

Decision making in elite level sporting competition is often regarded as distinguishing success from failure. As an intricate brain-based process it is unlike other physical processes because it is invisible and is typically only evidenced after the event. This case study shows how an individual achieved great success in elite level professional football through consistent positive decision making on and off the field of play. Through prolonged interviewing, Gary Neville, a player from Manchester United Football Club, explored personal behaviours, the structure and activities of deliberate practice and his professional choices in match preparation. His career-long devotion to purposeful organised practice was focused on cognition, physical preparation, context-relative physical action and refined repetition to optimise his mental comfort while enhancing his match day performances. This approach was underpinned by diligent personal and collective organisation and by concerted action. Results provide an insight into the categorical nature of his deliberate practice, sport-specific information processing and match-based decision making. At the operational level, his process was mediated by a complex mental representation of ongoing and anticipated game situations; these representations were continuously updated during each match. Allowing for the limitations of the design, implications are provided for developmental and educational coaching, match preparation, deliberate practice activity and improved use of the performance analysis software packages in professional football

Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort

Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk taker?” Risk takers (compared with controls) were more likely to be men, smokers, and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait that has a major impact on a range of common physical and mental health disorders