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The Association of College and Research Libraries Framework for Information Literacy for Higher Education has replaced the Information Literacy Competency Standards for Higher Education since 2016. The survey revealed how college and university librarians throughout the U.S. apply the Framework in their Information Literacy instruction, what barriers they encounter in applying the Framework, the lessons they have learned, and if they assess students' learning outcomes with the Framework. The findings indicate that other than course assignments, the Framework is librarians' most used document for their instruction. Despite the time constraints of the one-shot IL sessions, most surveyed librarians have adopted some Framework concepts in their instruction and have also managed to conduct assessment of student learning outcomes. Challenges still exist and librarians can benefit from more training to better communicate the Framework concepts to their constituents and to integrate the Framework in their instruction. This study depicts the status quo on the implementation of the ACRL Framework in the U.S. libraries from 2016 to 2020.

Many libraries subscribed to discovery services in the hope of boosting the use of their local collections such as print materials, DVDs, or even subscribed ejournals and ebooks that are separate from licensed databases. However, anecdotal evidence shows that the implementation of a discovery service will increase the usage of the vendors’ electronic resources instead. This study aims to find out how well local collections are promoted through EBSCO Discovery Service (EDS), Primo, Summon, and WorldCat Discovery Services (WorldCat) by comparing results of queries in the discovery services versus the number of actual listings from these libraries’ traditional catalogs. Authors conducted three searches based on real-life student assignments in science, history, and sociology in each of the four discovery services. To offset the bias of location configuration, ten libraries with the same discovery tool were selected for each search and total results are averaged. ANOVA and Tukey Honest Significant Difference statistical analyses show differences among the four discovery services investigated. Discovery tools promote local collections in different ways. Some tools provide the possibility to list libraries’ local collections before listing the vendor’s database items, but others are less inclined to do so.

Objective – Librarians at Rider University attempted to discern the basic information literacy (IL) skills of students over a two year period (2009-2011). This study aims to explore the impact of one-session information literacy instruction on student acquisition of the information literacy skills of identifying information and accessing information using a pretest/posttest design at a single institution. The research questions include: Do different student populations (in different class years, Honors students, etc.) possess different levels of IL? Does the frequency of prior IL Instruction (ILI) make a difference? Do students improve their IL skills after the ILI? Methods – The librarians at Rider University developed the test instruments over two years and administered them to students attending the ILI sessions each semester. The test was given to students as they entered the classroom before the official start-time of the class, and the test was stopped five minutes into the class. A pretest with five questions was developed from the 1st ACRL IL Standards. A few demographic questions were added. This pretest was used in fall 2009. In spring 2010, a second pretest was developed with five questions on the 2nd ACRL IL Standards. Students of all class years who attended ILI sessions took the pretests. In 2010-2011, the pretest combining the 10 questions used in the previous year was administered to classes taking the required CMP-125 Research Writing and the BHP-150 Honors Seminar courses. An identical posttest was given to those classes that returned for a follow-up session. Only the scores from students taking both pretests and posttests were used to compare learning outcomes. Results – Participants’ basic levels of IL skills were relatively low. Their skills in identifying needed resources (ACRL IL Standards 1) were higher than those related to information access (ACRL IL Standards 2). Freshmen in the Honors Seminar outperformed all other Rider students. No differences were found in different class years or with varying frequencies of prior IL training. In 2010-2011, students improved significantly in a few IL concepts after the ILI, but overall gains were limited. Limitations – Many limitations are present in this study, including the challenge of developing ideal test questions and that the pretest was administered to a wide variety of classes. Also not all the IL concepts in the test were adequately addressed in these sessions. These factors would have affected the results. Conclusions – The results defy a common assumption that students’ levels of IL proficiency correlate with their class years and the frequency of prior ILI in college. These findings fill a gap in the literature by supporting the anecdote that students do not retain or transfer their IL skills in the long term. The results raise an important question as to what can be done to help students more effectively learn and retain IL in college. The authors offer strategies to improve instruction and assessment, including experimenting with different pedagogies and creating different posttests for spring 2012.

Background: In the era of personalized medicine, a major challenge is harnessing longitudinal data across the cancer care continuum, which includes multimodal data sets of biologic, molecular, and clinical information about patients (pts) and their tumors. There is a growing need for new computing analytics, such as machine learning–an important tool in healthcare bio-informatics. We report our approach to building cancer disease models in an unbiased manner through utilization of a causal machine learning and simulation platform. Methods: The Swedish Cancer Institute (SCI) Personalized Medicine Research Program (PMRP) is a prospective registration protocol with the objective of establishing a centralized longitudinal, molecular, and phenotypic data repository. Since 2014, over 1,030 pts have been enrolled, having undergone next-generation sequencing (NGS) profiling of their tumors. Of these pts, we identified 100 breast cancer pts who also have detailed longitudinal clinical annotation within our SCI Breast Cancer Registry. All de-identified data, variables, and data points in the multimodal data types are integrated into normalized data frames to include demographics, cancer risks, tumor specifications, tumor sequencing, initial and subsequent cancer treatments, and outcomes data. A reverse engineering approach, via the Reverse Engineering and Forward Simulation (REFS) platform, is being utilized, focusing on discovering the complex causal mechanisms that determine which therapies will produce the best outcomes for an individual pt. This method goes beyond traditional approaches that rely on data correlations to match treatments to pts. The breast cancer causal model uncovers many of the possible combinations of causal relationships that drive outcomes and enables “what if?” simulations of a variety of interventions, across pts, to determine optimal therapies. Performance metrics and model robustness will be explored using a stratified, n-fold (e.g., 10-fold) cross-validation procedure, which is designed to provide an unbiased estimate of model generalization to new observations. Results: The causal model and simulations can elevate the providers' abilities to better understand treatment responses based on pts' unique clinical data and mutational statuses; study different treatment options to optimize management; and understand the complex interactions among variables that lead to a range of treatment outcomes. Conclusions: Knowledge generated from the simulations of the disease model can potentially streamline and support the clinical decision-making process, to include molecular tumor board deliberations, and ultimately assist providers in arriving at optimal treatment recommendations for pts. Citation Format: Henry Kaplan, Anna Berry, Kristine Rinn, Erin Ellis, George Birchfield, Tanya Wahl, Xiaoyu Liu, Mariko Tameishi, J D. Beatty, Patricia Dawson, Vivek Mehta, Anna Holman, Mary Atwood, Shlece Alexander, Candy Bonham, Lauren Summers, Iya Khalil, Boris Hayete, Diane Wuest, Wei Zheng, Yuhang Liu, Xulong Wang, Thomas David Brown. Machine learning approach to personalized medicine in breast cancer patients: Development of data-driven, personalized, causal modeling through identification and understanding of optimal treatments for predicting better disease outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5299.

We evaluate the CyberKnife (Accuray Incorporated, Sunnyvale, CA, USA) for non-invasive delivery of accelerated partial breast irradiation (APBI) in early breast cancer patients. Between 6/2009 and 5/2011, nine patients were treated with CyberKnife APBI. Normal tissue constraints were imposed as outlined in the National Surgical Adjuvant Breast and Bowel Project B-39/Radiation Therapy Oncology Group 0413 (NSABP/RTOG) Protocol (Vicini and White, 2007). Patients received a total dose of 30 Gy in five fractions (group 1, n = 2) or 34 Gy in 10 fractions (group 2, n = 7) delivered to the planning treatment volume (PTV) defined as the clinical target volume (CTV) +2 mm. The CTV was defined as either the lumpectomy cavity plus 10 mm (n = 2) or 15 mm (n = 7). The cavity was defined by a T2-weighted non-contrast breast MRI fused to a planning non-contrast thoracic CT. The CyberKnife Synchrony system tracked gold fiducials sutured into the cavity wall during lumpectomy. Treatments started 4-5 weeks after lumpectomy. The mean PTV was 100 cm(3) (range, 92-108 cm(3)) and 105 cm(3) (range, 49-241 cm(3)) and the mean PTV isodose prescription line was 70% for groups 1 and 2, respectively. The mean percent of whole breast reference volume receiving 100 and 50% of the dose (V(100) and V(50)) for group 1 was 11% (range, 8-13%) and 23% (range, 16-30%) and for group 2 was 11% (range, 7-14%) and 26% (range, 21-35.0%), respectively. At a median 7 months follow-up (range, 4-26 months), no acute toxicities were seen. Acute cosmetic outcomes were excellent or good in all patients; for those patients with more than 12 months follow-up the late cosmesis outcomes were excellent or good. In conclusion, the lack of observable acute side effects and current excellent/good cosmetic outcomes is promising. We believe this suggests the CyberKnife is a suitable non-invasive radiation platform for delivering APBI with achievable normal tissue constraints.