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In 2021, the assessment-data management librarian at Lehman College Library decided to conduct a privacy audit of the Library's public computers and networks. This audit comprised one of the Library's two annual formal assessments of resources and services. The American Library Association's (ALA) Library Privacy Checklist for Public Access Computers and Networks was selected to review 17 key items related to protecting user privacy and confidentiality. Faculty and staff from Circulation, Library Technology, and Online Learning identified 10 indicators needing work. Suggestions are provided for collaboratively resolving these issues and future steps are described to continuously maximize the online security of the campus community. [ABSTRACT FROM AUTHOR]

Statins are widely prescribed and highly susceptible to pharmacokinetic (PK)‐based drug–drug interactions (DDIs). To date, there has not been a comprehensive analysis of the basis upon which statin DDI recommendations in US Food and Drug Administration (FDA) prescribing information (PI) are derived. We have conducted such an analysis. We also assessed the degree of concordance of statin DDI recommendations in FDA PI and those provided in common tertiary clinical resources. We catalogued statin DDI information, including PK data and management recommendations, for statin precipitant drugs approved from 2010 to 2021, available from FDA PI and tertiary clinical resource databases. Recommendations were categorized and mapped with associated PK data to assess consistency in the PK basis for labeling recommendations. From the 80 precipitant drugs evaluated, 180 statin DDIs were identified in FDA PI. Dedicated clinical DDI studies were conducted for 54% (n = 97) of these DDIs and 34% (n = 61) of DDI recommendations were extrapolated from clinical data with other statins. Overall, we found that PK‐based statin recommendations were consistent across PI. These findings highlight regulatory precedence for translating information across statins without conducting dedicated clinical DDI studies, which may support future efforts toward streamlining the approach to investigation and labeling of statin DDIs. In addition, with the exception of some notable discrepancies, general concordance was observed between FDA and tertiary resources regarding "Dose Adjustment" and "Avoid Coadministration" recommendations. However, further analyses are warranted across other DDI pairs to determine whether discordance can routinely lead to different clinical recommendations depending on the drug information resource. [ABSTRACT FROM AUTHOR]

Background: Nursing interventions in the post‐operative time period including psychological and emotional support, adverse event education, and instructions for follow‐up care contribute patient satisfaction, safety, and quality of life. However, the time spent in the post‐anesthesia care unit (PACU) and hospital continues to shorten around the world to reduce health care spending and improve patient outcomes. Nurses conducting research during the important post‐operative recovery period need to utilize unique techniques and emerging technologies to contact, recruit and collect data outside of the hospital setting including the Research Electronic Data Capture (REDCap) platform. Aims: This paper describes the feasibility and acceptability, facilitators and barriers of the software application, REDCap, to complete a repeated‐measures, descriptive correlational study in patients undergoing outpatient breast cancer surgeries. Methods & Materials: The recruitment, data collection and storage were completed utilizing the secure REDCap Platform. The Institutional Research Board (IRB)‐approved study was a repeated‐measures, descriptive, correlational study with data collection at three time points. The data points aligned with important transitions and routine visits to improve data collection feasibility and increase relevance to clinical practice. Results: The sample consisted of women diagnosed with breast cancer undergoing breast conserving surgery between August 15 and October 15, 2020. There were 123 potential participants, of which 76 started the surveys and 75 participated (61%) responded and participated in the study on Post‐operative Day 1. Fifty‐nine participants (78%) completed the surveys on post‐operative Day 14. Discussion: As the frequency of outpatient treatment increases, nurses conducting post‐operative research will need to collect the data outside of the hospital setting. Conclusion: Email provides a method of studying new phenomena by recruiting participants, providing information about the study, and collecting results in a non‐traditional setting. REDCap provides a method to facilitate nursing research through a securely encrypted integrated process. [ABSTRACT FROM AUTHOR]

It is tempting to assume that because effectiveness metrics have free choice to assign scores to search engine result pages (SERPs) there must thus be a similar degree of freedom as to the relative order that SERP pairs can be put into. In fact that second freedom is, to a considerable degree, illusory. That is because if one SERP in a pair has been given a certain score by a metric, fundamental ordering constraints in many cases then dictate that the score for the second SERP must be either not less than, or not greater than, the score assigned to the first SERP. We refer to these fixed relationships as innate pairwise SERP orderings. Our first goal in this work is to describe and defend those pairwise SERP relationship constraints, and tabulate their relative occurrence via both exhaustive and empirical experimentation. We then consider how to employ such innate pairwise relationships in IR experiments, leading to a proposal for a new measurement paradigm. Specifically, we argue that tables of results in which many different metrics are listed for champion versus challenger system comparisons should be avoided; and that instead a single metric be argued for in principled terms, with any relationships identified by that metric then reinforced via an assessment of the innate relationship as to whether other metrics are likely to yield the same system‐versus‐system outcome. [ABSTRACT FROM AUTHOR]

Objective: The Interpersonal Theory of Suicide (ITS) could help identify differences in groups of suicidal adolescents and inform treatment. Method: Latent Profile Analysis (LPA) using thwarted belongingness (TB), perceived burdensomeness (PB), hopelessness, and capability was conducted on data from an at‐risk clinical sample (N = 500). The ITS prediction that changes in TB and PB are associated with changes in suicidal ideation was tested using admission and discharge data. Results: Latent Profile Analysis identified three profiles with increasing complexity and severity on ITS factors. The profiles were labelled low‐severity (7.6% of participants), moderate‐severity (45.2%), and high‐severity (47.2%). ITS predictions were partially supported for the full sample and only for the high‐severity and moderate‐severity subgroups, whereby changes in TB were significantly associated with changes in suicidal ideation over the course of treatment. However, changes in PB were only significant in the moderate‐severity subgroup, and none of the ITS predictions were supported in the low‐severity subgroup. Additionally, effect sizes for changes in TB and PB were modest in all analyses. Conclusions: Our findings highlight the importance reducing low belongingness in youth, which is a component of all supported interventions of youth suicide prevention. However, given the modest association of changes in ITS variables had with changes in suicidal ideation, it may be fruitful to elaborate on the relative importance on types of low belongingness or include other non‐ITS variables. [ABSTRACT FROM AUTHOR]

Objectives: Among nursing home (NH) residents with Alzheimer's disease (AD) and AD-related dementias (AD/ADRD), racial/ethnic disparities in quality of care exist. However, little is known about quality of life (QoL). This study examines racial/ethnic differences in self-reported QoL among NH residents with AD/ADRD. Methods: Validated, in-person QoL surveys from 12,562 long-stay NH residents with AD/ADRD in Minnesota (2012–2015) were linked to Minimum Data Set assessments and facility characteristics. Hierarchical linear models assessed disparities in resident-reported mean QoL score (range, 0–100 points), adjusting for case-mix and facility factors. Results: Compared to White residents, racially/ethnically minoritized residents reported significantly lower total mean QoL scores (75.53 points vs. 80.34 points, p <.001). After adjustment for resident- and facility-level characteristics, significant racial/ethnic differences remained, with large disparities in food enjoyment, attention from staff, and engagement domains. Discussion: Policy changes and practice guidelines are needed to address racial/ethnic disparities in QoL of NH residents with AD/ADRD. [ABSTRACT FROM AUTHOR]

The real-time monitoring on the risk status of the vehicle and its driver can provide the assistance for the early detection and blocking control of single-vehicle accidents. However, complex risk coupling relationship is one of the main features of single-vehicle accidents with high mortality rate. On the basis of investigating the coupling effect among multi-risk factors and establishing a safety management database throughout the life cycle of vehicles, single-vehicle driving risk network (SVDRN) with a three-level threshold was developed, and its topology features were analyzed to assessment the importance of nodes. To avoid the one-sidedness of single indicator, the multi-attribute comprehensive evaluation model was applied to measure the comprehensive effect of characteristic indicators for nodes importance. A algorithm for real-time monitoring of vehicle driving risk status was proposed to identify key risk chains. The result revealed that improper operation, speeding, loss of vehicle control and inefficient driver management were the sequence of top four risk factors in the comprehensive evaluation result of nodes importance (mean value = 0.185, SD = 0.119). There were minor differences of 0.017 in the node importance among environmental factors, among which non-standard road alignment had the larger value. The improper operation and non-standard road alignment were the highest combination correlation of factors affecting road safety, with the support of 51.81% and the confidence of 69.35%. This identification algorithm of key risk chains that combines node importance and its risk state threshold can effectively determine the high-frequency risk transmission paths and risk factors through multi-vehicle test, providing a basis for centralization management of transport enterprises. [ABSTRACT FROM AUTHOR]

Framework analysis methods (FAMs) are structured approaches to qualitative data analysis that originally stem from large-scale policy research. A defining feature of FAMs is the development and application of a matrix-based analytical framework. These methods can be used across research paradigms and are thus particularly useful tools in the health professions education (HPE) researcher's toolbox. Despite their utility, FAMs are not frequently used in HPE research. In this AMEE Guide, we provide an overview of FAMs and their applications, situating them within specific qualitative research approaches. We also report the specific characteristics, advantages, and disadvantages of FAMs in relation to other popular qualitative analysis methods. Using a specific type of FAM—i.e. the framework method—we illustrate the stages typically involved in doing data analysis with an FAM. Drawing on Sandelowski and Barroso's continuum of data transformation, we argue that FAMs tend to remain close to raw data and be descriptive or exploratory in nature. However, we also illustrate how FAMs can be harnessed for more interpretive analyses. We propose that FAMs are valuable resources for HPE researchers and demonstrate their utility with specific examples from the HPE literature. [ABSTRACT FROM AUTHOR]

Simple Summary: The major impact from this work is two-fold, first in providing a list of reproducible candidate drugs nominated against multiple glioblastoma (GBM) datasets, and second in providing drug–gene biomarkers of interest for GBM as well as reproducible computational pipelines for identifying drug–biomarker leads that could be extended to other cancer types beyond GBM. We believe the research community will be interested and can utilize our results here for the further development of biomarker-specific drug therapies in both GBM as well as other disease types. Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and most common brain cancer in adults. Not only is there an urgent need to identify efficacious therapeutics, but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations. We built patient drug response models by integrating patient tumor transcriptome data with high-throughput cell line drug screening data as well as Bayesian networks to infer relationships between patient gene expression and drug response. Through these discovery pipelines, we identified agents of interest for GBM to be effective across five independent patient cohorts and in a mouse avatar model: among them are a number of MEK inhibitors (MEKis). We also predicted phosphoglycerate dehydrogenase enzyme (PHGDH) gene expression levels to be causally associated with MEKi efficacy, where knockdown of this gene increased tumor sensitivity to MEKi and overexpression led to MEKi resistance. Overall, our work demonstrated the power of integrating computational approaches. In doing so, we quickly nominated several drugs with varying known mechanisms of action that can efficaciously target GBM. By simultaneously identifying biomarkers with these drugs, we also provide tools to select the right patient populations for subsequent evaluation. [ABSTRACT FROM AUTHOR]

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans , Drosophila , zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML). [ABSTRACT FROM AUTHOR]

FungiDB (https://fungidb.org) serves as a valuable online resource that seamlessly integrates genomic and related large-scale data for a wide range of fungal and oomycete species. As an integral part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org), FungiDB continually integrates both published and unpublished data addressing various aspects of fungal biology. Established in early 2011, the database has evolved to support 674 datasets. The datasets include over 300 genomes spanning various taxa (e.g. Ascomycota, Basidiomycota, Blastocladiomycota, Chytridiomycota, Mucoromycota, as well as Albuginales, Peronosporales, Pythiales, and Saprolegniales). In addition to genomic assemblies and annotation, over 300 extra datasets encompassing diverse information, such as expression and variation data, are also available. The resource also provides an intuitive web-based interface, facilitating comprehensive approaches to data mining and visualization. Users can test their hypotheses and navigate through omics-scale datasets using a built-in search strategy system. Moreover, FungiDB offers capabilities for private data analysis via the integrated VEuPathDB Galaxy platform. FungiDB also permits genome improvements by capturing expert knowledge through the User Comments system and the Apollo genome annotation editor for structural and functional gene curation. FungiDB facilitates data exploration and analysis and contributes to advancing research efforts by capturing expert knowledge for fungal and oomycete species. [ABSTRACT FROM AUTHOR]

Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a Digital Speech Assessment Instrument to evaluate the phonological and motor speech skills of Brazilian Portuguese–speaking children and adolescents through six tasks: (a) word naming, (b) word imitation, (c) pseudoword imitation, (d) word repetition, (e) pseudoword repetition, and (f) diadochokinesis. Method: The assessment instrument was developed and validated in a seven-step process, beginning with stimulus selection and ending with Pilot Study 1 (involving children and adolescents with typical speech development) and Pilot Study 2 (involving children and adolescents with speech sound disorders [SSDs]). Participants of the study included expert and nonexpert judges, children, and adolescents. Results: The Digital Speech Assessment Instrument contains 91 real words (61 in the naming task, 51 in the imitation task, and five in the repetition task), 26 pseudowords (26 in the imitation task and four in the repetition task), and six stimuli for diadochokinesis. The test contains stimuli in the form of images as well as audio and video recordings and allows for the analysis and storage of participant data in a virtual database. Conclusions: This study described the development of the Digital Speech Assessment Instrument, available in Brazilian Portuguese for the evaluation of several aspects of speech production (including word and pseudoword naming, imitation, and repetition and diadochokinesis). The test was developed for children aged 2;0 (years;months) to 17;11 and is administered fully online. In the future, the instrument can be used to provide a timely and accurate diagnosis of SSDs. [ABSTRACT FROM AUTHOR]

In 2016 WHO launched the priority assistive products list (APL) consisting of 50 products and recommended that using this as a reference, countries should develop their own contextually relevant national APLs. This paper describes the development of Malawi's APL. Two hundred and ninety-six persons with disabilities participated in a rapid Assistive Technology Assessment (rATA) survey. Six focus group discussions (FGDs) with people with various types of disabilities were conducted. The rATA questionnaire and FGDs collected data on assistive products (APs) participants used, APs they needed and the challenges they experienced. Data collection was done in six districts spread across the three regions in Malawi. All age groups were included in the survey. Persons with disabilities aged less than 18 participated but went with their guardians. All persons who participated in this study provided consent. Survey and FGDs results were presented at an APL consensus meeting with policymakers, service providers, disabled peoples' organizations and development partners in the disability sector. Based on the results and further discussions, a consensus was reached on the priority APs for Malawi. More than a third of respondents used wheelchairs (32%), followed by auxiliary crutches (25%), walking sticks (13%), reading glasses (11%), prosthesis (10%), elbow crutches (9%) and orthosis (8%). There is also a high demand for products such as pull-up underwear (incontinence products) (79%), hearing aids (70%), reading glasses (59%) and diapers (63%). After intensive discussions during a consensus meeting, an agreement was reached on the 22 priority APs for Malawi. There is a wide range of APs being used by people with different functional limitations in Malawi. There is also a demand for APs that are not readily available. When developing an APL, the list should include products in use, those in demand, and those recommended by service providers. Following the development of the priority assistive products list (APL) by WHO, member states should develop their own contextually based APL. The development of the APL should be based on research evidence. All key stakeholders including persons with disabilities and other functional limitations, government, and development partners should participate in this process. The APL should be part of the national health system or community services. The Department of Disability and Elderly Affairs in the Ministry of Gender, Community Development, being the Government of Malawi line ministry coordinating disability issues participated actively in this study including inviting participants in the stakeholders' validation workshop. [ABSTRACT FROM AUTHOR]

Visual impairment-related disabilities have become increasingly pervasive. Current reports estimate a total of 36 million persons with blindness and 217 million persons with moderate to severe visual impairment worldwide. Assistive technologies (AT), including text-to-speech software, navigational/spatial guides, and object recognition tools have the capacity to improve the lives of people with blindness and low vision. However, access to such AT is constrained by high costs and implementation barriers. More recently, expansive growth in mobile computing has enabled many technologies to be translated into mobile applications. As a result, a marketplace of accessibility apps has become available, yet no framework exists to facilitate navigation of this voluminous space. We developed the BLV (Blind and Low Vision) App Arcade: a fun, engaging, and searchable curated repository of app AT broken down into 11 categories spanning a wide variety of themes from entertainment to navigation. Additionally, a standardized evaluation metric was formalized to assess each app in five key dimensions: reputability, privacy, data sharing, effectiveness, and ease of use/accessibility. In this paper, we describe the methodological approaches, considerations, and metrics used to find, store and score mobile applications. The development of a comprehensive and standardized database of apps with a scoring rubric has the potential to increase access to reputable tools for the visually impaired community, especially for those in low- and middle-income demographics, who may have access to mobile devices but otherwise have limited access to more expensive technologies or services. A wide array of assistive mobile applications now serve as low cost, convenient, and effective alternatives to standard tools in the rehabilitation domain. Given an extensive (and growing) marketplace of assistive apps, we highlight the importance of developing standardized evaluation frameworks that serve to assess the merit, functionality, and accessibility of tools in respective rehabilitation fields. To provide an introduction to a novel resource accessible to the public to exhibit verified and reliable assistive apps for the visually impaired community, especially for those in low- and middle-income demographics who may not have access to common technologies and services. [ABSTRACT FROM AUTHOR]

Assess prevalence of serious mental illness (SMI) alone, and co-occurring with Alzheimer disease and related dementias (ADRD), among Medicare beneficiaries in assisted living (AL). Examine the association between permanent nursing home (NH) placement and SMI, among residents with and without ADRD. 2018-2019 retrospective cohort of Medicare beneficiaries in AL. Residents were followed for up to 2 years to track their NH placement. We used data from the Medicare Enrollment Database, the Medicare Beneficiary Summary File, Minimum Data Set, and a national directory of state-licensed AL communities. AL residents were identified using a validated, previously reported 9-digit zip code methodology. A cross-sectional study sample included 289,350 Medicare beneficiaries in 17,265 AL communities across 50 states and in the District of Columbia. The outcome was permanent NH placement: a continuous stay for more than 90 days. Key independent variable was presence of SMI—schizophrenia, bipolar disorder, and major depression. Other covariates included sociodemographic factors and presence of other chronic conditions, including ADRD. A linear probability model with robust SEs, and AL-level random effects, was used to test the association between SMI diagnoses, ADRD, and their interactions on NH placement. More than half (55.65%) of AL residents had a diagnosis of SMI, among them 93.2% had major depression, 28.5% schizophrenia, and 22.2% bipolar disorder. Individuals with schizophrenia and bipolar disorder had a significantly lower probability of NH placement, a 32% and a 15% decrease relative to the cohort mean, respectively. Placement risk was significantly greater for residents with ADRD compared to those without, increasing for those who also had schizophrenia or bipolar disorder, 12.9% and 1.5% relative to the sample mean, respectively. Presence of schizophrenia and bipolar disorder, in conjunction with ADRD, significantly increases the risk of long-term NH placement, suggesting that ALs may not be well prepared to care for these residents. [ABSTRACT FROM AUTHOR]

Data users (researchers, scientists) in healthcare institutions need access to integrated healthcare data to conduct timely analysis of diseases to serve the right population at the right time. However, preserving patient privacy and timely access to quality healthcare data is a critical challenge. Current healthcare data governance systems are largely manual. Besides, processing process data requests is extremely slow, often taking months. To address this gap, we designed an honest-broker-based healthcare application to support data users in accessing healthcare data securely and to design a comprehendible process of data governance for data users. This study applied two iterations of a user experience (UX) evaluation of an honest broker prototype. Results show that participants found the new system promising for their research prospects. Implications suggest that technological knowledge should not be a requirement for using healthcare applications to promote broader adoption in the community. This study highlights the necessity of a process to balance the control of access to sensitive data between data providers and users as well as to educate data users on data privacy. Iterative UX studies can be a fruitful approach in gradually uncovering problems and improving the design of complex systems. [ABSTRACT FROM AUTHOR]

The purpose of this pre-registered study (Peltier & McKenna) was to conceptually replicate if the truncation of the ordinate and DPPXYR increased analysts' estimation of a functional relation and magnitude of treatment effect. Visual analysts (n = 27) evaluated eight data sets reporting null (n = 2), small (n = 2), moderate (n = 2), and large (n = 2) effects. Each data set was graphed six times with manipulations of the ordinate and DPPXYR, resulting in 48 ABAB graphs. We estimated two separate three-level mixed effect models with variations nested in datasets and nested in participants to evaluate the impact of graph characteristics for (1) confidence in determining a functional relation and (2) the estimated magnitude of the treatment effect. We included ordinate scaling and DPPXYR at level 1 and graph effect size at level 2, including all interactions. Overall, graph manipulation consistently did not impact confidence in a functional relation. Results suggest mixed findings for graph manipulation on the estimated magnitude of the treatment effect. Findings will be couched in current literature and recommendations for graph construction and future research will be discussed. [ABSTRACT FROM AUTHOR]

Missing data is inevitable in single-case experimental designs (SCEDs) studies due to repeated measures over a period of time. Despite this fact, SCEDs implementers such as researchers, teachers, clinicians, and school psychologists usually ignore missing data in their studies. Performing analyses without considering missing data in an intervention study using SCEDs or a meta-analysis study including SCEDs studies in a topic can lead to biased results and affect the validity of individual or overall results. In addition, missingness can undermine the generalizability of SCEDs studies. Considering these drawbacks, this study aims to give descriptive and advisory information to SCEDs practitioners and researchers about missing data in single-case data. To accomplish this task, the study presents information about missing data mechanisms, item level and unit level missing data, planned missing data designs, drawbacks of ignoring missing data in SCEDs, and missing data handling methods. Since single imputation methods among missing data handling methods do not require complicated statistical knowledge, are easy to use, and hence are more likely to be used by practitioners and researchers, the present study evaluates single imputation methods in terms of intervention effect sizes and missing data rates by using a real and hypothetical data sample. This study encourages SCEDs implementers, and also meta-analysts to use some of the single imputation methods to increase the generalizability and validity of the study results in case they encounter missing data in their studies. [ABSTRACT FROM AUTHOR]

The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data. [ABSTRACT FROM AUTHOR]

Background: Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. Methods: In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. Results: In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. Conclusions: The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, academic libraries saw a shift toward remote learning leading to increased dependency on electronic resources. This article explores the implications of the pandemic on electronic resource management, focusing on modifications in budget allocation, licensing, accessibility, and staffing. Drawing from a data set of 120 survey responses in 2023 in the United States, this article illustrates the challenges academic libraries faced and changes made during the pandemic. These findings reiterate the essential role of electronic resources and emphasize the need for academic libraries to continually adapt their management strategies to meet user needs in the post-pandemic era. [ABSTRACT FROM AUTHOR]

SUSHI is a widely used protocol in academic libraries. This article discusses maintaining SUSHI vendor accounts in Alma, troubleshooting irregularities, and finding technical support. This article also includes sample analyses for the ACRL survey 60B Digital/Electronic Circulation or Usage and 63 E-serials Usage using COUNTER 5 reports. [ABSTRACT FROM AUTHOR]

Routinely collected data help estimate the prevalence of autism spectrum disorder (ASD) in jurisdictions without active autism surveillance. We created a population-based cohort of 1,211,834 children born in 2002–2015 in New South Wales, Australia using data linkage. Children with ASD were identified in three datasets – disability services, hospital admissions, and ambulatory mental health data. The prevalence of ASD in the cohort was 1.3% by age 12 and prevalence at age 6 increased an average of 4.1% per year (95% Confidence Interval, 3.3%, 4.8%). Most children with ASD were identified in disability services data (87%), although data linkage identified 1,711 additional cases that were more likely female, older at first contact, and living in major cities and less disadvantaged areas. [ABSTRACT FROM AUTHOR]

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare. [ABSTRACT FROM AUTHOR]

The field of business management involves a large amount of data and information sources, including market data, customer data, supply chain data, etc. In order to quantify and analyze different resources, help enterprises better plan and allocate resources, and improve resource utilization efficiency, a clustering analysis based digital resource integration algorithm for business management is studied. Build a business management digital resource integration framework, including data layer, integration layer, and storage layer, to integrate and store data from different sources of business management databases, thereby facilitating unified management and utilization of digital resources by enterprises. The data layer collects data from different business management databases and stores it in the database according to different sources; The integration layer preprocesses the collected data, simply fixes errors and missing information in the data, and improves data quality. Adopting a feature extraction method based on the projection direction uncorrelation strategy of the labeled power set conversion method, the useful feature information of digital resources in enterprise management can be effectively extracted; Based on the two-step clustering analysis method, business management digital resources are clustered according to similar characteristics to complete the classification and integration of business management digital resources, and improve the efficiency of resource utilization; The storage layer adopts the Security Information Diffusion Algorithm (IDA) storage model to store integrated and classified digital resources managed by enterprises, ensuring data security and effectively preventing data leakage and illegal access. The experimental results show that the digital resource structure of business management integrated by this algorithm is clear, with a data redundancy of less than 8% and a difference of less than 11%. The time consumption for data integration is less than 2.11 minutes, indicating good resource integration ability. [ABSTRACT FROM AUTHOR]

Objectives: The aims of the study are to describe the frequency that functional goals are documented on the Minimum Data Set and to identify resident characteristics associated with meeting or exceeding discharge goals. Methods: We selected Medicare fee-for-service beneficiaries admitted to a skilled nursing facility within 3 days of hospital discharge fromOctober 1, 2018, to December 31, 2019 (N = 1,228,913). The admission Minimum Data Set was used to describe the discharge goal scores for seven self-care and 16 mobility items. We used the eight self-care and mobility items originally included in a publicly reported qualitymeasure to calculate total scores for discharge goals, admission performance, and discharge performance (n = 371,801). Results: For all self-care items, more than 70%of residents had a goal score of 1-6 points documented on the admissionMinimum Data Set. Chair/bed-to-chair transfer had the highest percentage of residents with a score of 1-6 points (77.1%) and walking up/down 12 steps had the lowest (23.2%). Approximately 44% of residents had a discharge performance score that met or exceeded their goal score. Older age, urinary incontinence, and cognitive impairment had the lowest odds of meeting or exceeding discharge goals. Conclusions: Assessing a resident's functional goals is important to providing patient-centered care. This information may help skilled nursing facilities determine whether a resident has made meaningful functional improvements. [ABSTRACT FROM AUTHOR]

BACKGROUND: Behavioral health disorders (BHDs) can often be exacerbated in the setting of cancer. We sought to define the prevalence of BHD among cancer patients and characterize the association of BHD with surgical outcomes. STUDY DESIGN: Patients diagnosed with lung, esophageal, gastric, liver, pancreatic, and colorectal cancer between 2018 and 2021 were identified within Medicare Standard Analytic Files. Data on BHD defined as substance abuse, eating disorder, or sleep disorder were obtained. Postoperative textbook outcomes (ie no complications, prolonged length of stay, 90-day readmission, or 90-day mortality), as well as in-hospital expenditures and overall survival were assessed. RESULTS: Among 694,836 cancer patients, 46,719 (6.7%) patients had at least 1 BHD. Patients with BHD were less likely to undergo resection (no BHD: 23.4% vs BHD: 20.3%; p < 0.001). Among surgical patients, individuals with BHD had higher odds of a complication (odds ratio [OR] 1.32 [1.26 to 1.39]), prolonged length of stay (OR 1.36 [1.29 to 1.43]), and 90-day readmission (OR 1.57 [1.50 to 1.65]) independent of social vulnerability or hospital-volume status resulting in lower odds to achieve a TO (OR 0.66 [0.63 to 0.69]). Surgical patients with BHD also had higher in-hospital expenditures (no BHD: $16,159 vs BHD: $17,432; p < 0.001). Of note, patients with BHD had worse long-term postoperative survival (median, no BHD: 46.6 [45.9 to 46.7] vs BHD: 37.1 [35.6 to 38.7] months) even after controlling for other clinical factors (hazard ratio 1.26 [1.22 to 1.31], p < 0.001). CONCLUSIONS: BHD was associated with lower likelihood to achieve a postoperative textbook outcome, higher expenditures, as well as worse prognosis. Initiatives to target BHD are needed to improve outcomes of cancer patients undergoing surgery. [ABSTRACT FROM AUTHOR]

Librarians support researchers by promoting open science and open data practices. This article explores five freely available tools that support and facilitate open science practices. Open Science Framework provides a platform for project management, data sharing, and data storage. OpenRefine cleans and formats data. DMPTool has templates for data management and sharing plans that comply with funder mandates. The NIH Common Data Elements is a repository for standardized data elements, and finally, the NLM Scrubber is a tool for de-identifying clinical text data. Information professionals can add these tools to their repertoire and share them with researchers at their institution. [ABSTRACT FROM AUTHOR]

Previous research has established that the success of strikes, and social movements more broadly, depends on their ability to garner support from the public. However, there is scant published research investigating the response of the public to strike action by healthcare workers. In this study, we address this gap through a study of public responses to UK nursing strikes in 2022–2023, using a data set drawn from Twitter of more than 2300 publicly available tweets. We focus on negative tweets, investigating which societal discourses social media users draw on to oppose strike action by nurses. Using a combination of corpus‐based approaches and discourse analysis, we identified five categories of opposition: (i) discourse discrediting nurses; (ii) discourse discrediting strikes by nurses; (iii) discourse on the National Health System; (iv) discourse about the fairness of strikers' demands and (v) discourse about potential harmful impact. Our findings show how social media users operationalise wider societal discourses about the nursing profession (e.g., associations with care, gender, vocation and sacrifice) as well as recent crises such as the Covid‐19 pandemic to justify their opposition. The results also provide valuable insights into misconceptions about nursing, strike action and patient harm, which can inform strategies for public communication. [ABSTRACT FROM AUTHOR]

We applied a novel hierarchical Bayesian weighted quantile sum (HBWQS) regression to combine data across 3 study sites to examine associations between prenatal exposure to metals and cognitive functioning in childhood. Data from 326 mother-child dyads enrolled in an ongoing cohort study, the Programming of Intergenerational Stress Mechanisms (PRISM) Study, based in New York, New York (recruitment in 2013–2020) and Boston, Massachusetts (recruitment 2011–2013), and the First Thousand Days of Life (FTDL) cohort study (recruitment 2012–2019), based in northern Virginia, were used. Arsenic, cadmium, manganese, lead, and antimony were measured in urine collected during pregnancy. Cognitive functioning was assessed in children aged 3–11 years using the National Institutes of Health Toolbox Cognition Battery. The HBWQS regression showed a negative association between the urinary metal mixture and the Cognition Early Childhood Composite Score in the PRISM New York City (β = −3.67, 95% credible interval (CrI): −7.61, −0.01) and FTDL (β = −3.76, 95% CrI: −7.66, −0.24) samples, with a similar trend in the PRISM Boston sample (β = −3.24, 95% CrI: −6.77, 0.144). We did not detect these associations in traditionally pooled models. HBWQS regression allowed us to account for site heterogeneity and detect associations between prenatal metal-mixture exposure and cognitive outcomes in childhood. Given the ubiquity of metals exposure, interventions aimed at reducing prenatal exposure may improve cognitive outcomes in children. This article is part of a Special Collection on Environmental Epidemiology. [ABSTRACT FROM AUTHOR]

Objective: This study evaluated score reliability of the Professional Quality of Life Scale (ProQoL) when contextualizing help to a relevant derivative. Method: The researchers evaluated score reliability across three datasets among school-based professionals (n = 122), teachers (n = 216), and mental health professionals (n = 543) using Cronbach's alpha, McDonald's omega, inter-item correlations and visual analysis. Results: Score reliability was acceptable across samples. However, item analysis indicated variability in responses indicating participants' extracting different meaning or relevance to the items. Terminology changes may extract different meaning based on profession resulting in measurement error. Conclusions: Findings highlight use of one-word derivative changes to items may impact score reliability based on the professional activities represented. Researchers should adjust item terminology for clarity and relevance to all the professions represented in the sample. Evidence of the nature of professional activities with client or student trauma to secondary traumatic stress is presented. This study evaluates score reliability of the ProQoL when contextualizing help to a derivative relevant to the profession. Internal consistency may differ across professions as participants extract different item meaning, directly impacting score reliability. Researchers and practitioners using the ProQoL should review items for clarity and relevance to the professionals represented. [ABSTRACT FROM AUTHOR]

Estimates of the price or value of the individual journals within a full-text database may be useful to librarians engaged in serials reviews or other collection development projects, to scholars investigating the determinants of journal prices, and to publishers seeking to rationalize their pricing strategies. This paper evaluates six methods of apportioning the cost of a full-text database among the individual journals in the database--methods based on variables such as journal size, total citations, Journal Impact Factor (JIF) percentile, and single-journal list price. Each method is evaluated based on how well the resulting prices can be predicted by the determinants of journal prices identified in previous research. Although the six methods yield similar results, the single best option is to use price estimates that account for JIF percentile. If citation data are not available and cannot be estimated, the best alternative is to rely on the equal-value assumption--to split the total price equally among the wanted journals in the database. [ABSTRACT FROM AUTHOR]

The relationship between affect and cognition is a central topic of interest in psychology. In this study, we focus on the relationship of affect valence and activation with construal level. Existing findings are inconsistent. We propose that these inconsistencies may result from the interdependency of affect valence and activation and propose that valence and activation interact in predicting construal level. Specifically, we propose that, overall, valence has a positive effect on construal level, and that activation accentuates the effect of valence. We test our predictions through a text analysis of event descriptions in four data sets: TripAdvisor hotel reviews (N = 330,853); Yelp restaurant reviews (N = 648,974); Amazon Digital Music reviews (N = 1,518,409); and Twitter Negative Emotions database (N = 8,756). Results confirm the predicted V‐shaped relationship between valence and activation, and the moderating effect of activation on the relationship between valence and construal level. We discuss these findings and the inconsistencies across studies. [ABSTRACT FROM AUTHOR]

Libraries at academic institutions have been involved in research data management services (RDMS) for more than ten years. Institutions have adapted their service models in response to funder, journal, and federal mandates that have consequently increased RDMS demand. However, institutions that hope to start or grow their services may have difficulty extrapolating from the published literature, as few studies recount RDMS development strategies. [ABSTRACT FROM AUTHOR]

Background: Understanding the principles and practice of research by health care professionals helps to improve the evidence base for palliative care practice and service delivery. Research is a core competency in palliative care that enables the identification and addressing of problems for patients and their families, establishes best practice and improves ways to manage pain, other symptoms, and concerns. This work was undertaken in the research for all palliative care clinicians (RESPACC) Erasmus+ project. Aim: To identify and develop a set of core research competencies within a structured framework for all members of the multidisciplinary palliative team. Design: A multi-method approach was used including rapid review of literature, Nominal Group Technique, and expert consultation. A quiz to self-assess research competencies outlined within the Framework was produced. Results: The Framework includes 17 competencies organized in 7 domains: The clinical context, Scientific thinking and research design, Ethics and regulatory framework for research, Study and site management, Data management and informatics, Communication and relationships, and Research leadership. In the consultation process 6 of the 17 competencies were considered as required by each individual team member, and 3 to be present within the palliative care team. Conclusion: Using a multi-method approach, the first Palliative Care Research Framework identifying core research competencies for palliative care clinicians has been developed. The aim of the framework was not to transform palliative care clinicians into researchers, but to ensure that clinicians understand the important role of research and its integration into clinical practice. [ABSTRACT FROM AUTHOR]

Background: Preeclampsia (PE) is a disease that seriously threatens maternal and fetal health. Appropriate autophagy can shield the placenta from oxidative stress, but its role in PE is unclear. Objective: To identify potential autophagy‐related genes in PE. Methods: Microarray datasets from the Gene Expression Omnibus database, compassing the test dataset GSE10588, along with validation datasets GSE4707 and GSE60438 GPL10558, were utilized. Differentially expressed genes (DEGs) were identified using the limma R package, intersected with autophagy‐related genes. Hub genes were obtained using the Cytoscape software and analyzed via gene set enrichment analysis (GSEA). The diagnostic capability of hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Analysis of immune cell infiltration was conducted using single‐sample gene set enrichment analysis (ssGSEA) and CIBERSORT methods. Placental tissues were collected from 10 normal pregnant women and 10 preeclamptic pregnant women, and the expression of hub genes was validated through immunohistochemistry and western blot analysis. Results: Analysis of the microarray data identified 2224 DEGs, among which 26 were autophagy‐related DEGs identified through intersection with autophagy genes. Ten hub genes were identified. Immune cell infiltration analysis suggested the potential involvement of T regulatory cells (Tregs), natural killer cells, neutrophils, and T follicular helper cells in the pathogenesis of PE. ROC curve analysis indicated promising diagnostic capabilities for EGFR and TP53. Additionally, levels of EGFR and TP53 were significantly higher in placental tissue from PE pregnancies compared to normal pregnancies. Conclusion: EGFR and TP53 may play a role in PE by influencing autophagy. [ABSTRACT FROM AUTHOR]

Bryophytes are important elements of plant communities in most habitat types worldwide. However, due to their relatively small size and difficult identification, bryophyte data are missing in many plant community studies. The objective of the present study is to increase the knowledge about EU Habitat directive bryophyte species distribution and conservation in Latvia. We compiled data from the Natural Data Management System OZOLS database about four EU Habitat Directive bryophyte species: Buxbaumia viridis, Dicranum viride, Hamatocaulis vernicosus and H. lapponicus, all indicators of high–value habitats in Latvia. The most common EU Habitat Directive species in this group was Buxmaumia viridis, which showed a significantly higher occurrence in the Western taiga habitat. Most of Hamatocaulis lapponicus and H. vernicosus localities were registered in protected areas with a high and moderate degree of protection. Most of Buxbaumia viridis and Dicranum viride localities were registered outside of the protected areas with a low degree of protection. Our results showed that EU Habitat Directive bryophyte species conservation in the long–term in Latvia is unclear, especially for B. viridis and D. viride populations. In future studies, we recommend initiating large-scale protected bryophyte monitoring programs and population studies that could contribute to plant community and global climate change research. [ABSTRACT FROM AUTHOR]

Purpose: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI reports in a low-resource language. Methods: We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments. Results: The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions. Conclusion: The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization. [ABSTRACT FROM AUTHOR]

Generation of reliable data underpins the effectiveness of Occupational Health and Safety (OHS) surveillance systems. Despite the importance of understanding OHS data systems, there are few papers that provide overviews of their structure and/or content. This paper introduces a basic framework for assessing OHS data systems that will be of use to researchers internationally. We applied this approach to assess the Irish OHS data system by undertaking a data mapping exercise. We developed a checklist based on recommendations of monitoring and measurement of OHS proposed by the National Academies of Sciences, Engineering, and Medicine (USA). An assessment of published reports that present systematic OHS surveillance data was undertaken to identify the institutions or organisations responsible for collecting and curating the data, their remit, and, associated with this, their respective case definitions. We then provide an overview of the variables collected and these are then mapped against the checklist. The assessment highlights that whilst the farm fatalities dataset provides complete coverage of all fatalities, regardless of age or employment status, the same is not true of the three non-fatal injuries datasets reviewed. There are important differences in the data collection methods and, associated with this, which populations are covered. The assessment approach provides valuable insights into the strengths and weaknesses of a critical element of OHS surveillance systems, namely the production of datasets. This knowledge is important for researchers as understanding the data that informs their research is fundamental to good science. It is critical for policy-makers and other stakeholders to understand the strengths and weaknesses on which OHS policy, strategies, or education and training interventions are developed. [ABSTRACT FROM AUTHOR]

Agriculture is a hazardous industry with undocumented injury events. Credible surveillance measures are critical for this industry, especially to guide injury prevention programs with targeted recommendations for specific commodity groups and populations. This multi-phase study explored the feasibility for two state agency databases, the Ohio Bureau of Workers' Compensation (BWC) Program and the Emergency Medical Services Incident Reporting System (EMSIRS), to augment the state's Bureau of Labor Statistics (BLS) annual reports. BWC data described injury claims in agricultural workplaces from 1999 to 2008. State EMSIRS data described the types of medical emergencies for which EMS services were requested to Ohio farms in 2013–2014. Descriptive analyses were performed on each distinctive source. Over 14,000 BWC claims were analyzed, with primary nature of injury identified as sprains and strains of bodily extremities; falls were the most common cause of injury. The EMSIRS data provided 1,376 cases, where EMS services were requested to Ohio farms at injury onset. Some cases had possibility to be excluded in CFOI or employment claims data, with 24% patients 65 years and older and 6% children 13 years and younger. The primary cause of injury was falls, and the highest reported injury type was blunt trauma. Both BWC and EMSIRS databases showed the potential to enhance Ohio's agricultural surveillance data with viable information not found in previously used systems. Each agency database had its own merits to further clarify and quantify morbidity. When used together, these sources enrich surveillance statistics to describe Ohio's agricultural injury incidents. [ABSTRACT FROM AUTHOR]

In this paper, a network model named DCC-Net based on infrared image sensor is developed for sports data management. Optical imaging plays an important role in the collection and analysis of sports data, but the traditional methods have limitations in the aspects of illumination change and motion blur. To solve these problems, we propose a DCC-net network model based on infrared image sensor. The background of this study is to improve the efficiency of collection and management of sports data in order to promote the improvement of athletes' training and competitive performance. We use a combination of convolutional neural networks and recurrent neural networks to extract and understand motion information in infrared images. The experimental results show that the DCC-net network model has better performance than the traditional optical method in the aspects of illumination variation and motion blur, and provides more accurate and stable motion data. Therefore, DCC-net network model based on infrared image sensor is an effective sports data management tool, which can provide more comprehensive and accurate training and competitive support for athletes and coaches. [ABSTRACT FROM AUTHOR]

This case study examines the decision-making process of library leaders and administrators in the selection, procurement, and implementation of Ex Libris Alma/Primo as their library services platform (LSP). The authors conducted a survey of libraries and library consortia in Canada and the United States who have implemented or plan to implement Alma. The results show that most libraries use both request for information (RFI) and request for proposal (RFP) in their system selection process, but the vendor-offered training is insufficient for effective operation. One-third of the libraries surveyed are considering switching to open-source options for their next automation system. These insights can benefit libraries and library consortia in improving their technological readiness and decision-making processes. [ABSTRACT FROM AUTHOR]