Fields, features, and filters: how dating applications construct sexual fields and romantic and erotic capital

Young adults have gone from consulting matchmakers to app-makers, as they seek ways to meet sexual and romantic partners. Each dating application is constructed in a distinctive manner, creating unique “sexual fields” on each platform. In this study, I consider the affordances of four dating applications—the structure of profiles, available search filters, and application features—in order to understand how dating applications construct sexual fields and emphasize different forms of erotic and romantic capital. I find that which qualities constitute capital differs depending on the platform politics of the application, and the sexual fields vary according to the intended audience and outcome of the rules for interactions on the app. Such an analysis reveals how forces of heterosexism, racism, and classism operate in modern partner-seeking, both romantic and sexual.

OP128 Improving Literature Searching For Evidence On Health Apps: The National Institute For Health And Care Excellence (NICE) MEDLINE And Embase (Ovid) Health Apps Search Filters

IntroductionLiterature searching for evidence on apps in bibliographic databases is challenging because they are often described with inconsistent terminology. Information Specialists from the United Kingdom's National Institute for Health and Care Excellence (NICE) have developed validated search filters for retrieving evidence about apps from MEDLINE and Embase (Ovid) reliably.MethodsMedical informatics journals were hand-searched to create a ‘gold standard’ set of app references. The gold standard set was divided into two sets. The development set provided the search terms for the filters. The filters were validated by calculating their recall against the validation set. Target recall was >90%.A case study was then conducted to compare the number-needed-to-read (NNR) of the filters with previous non-validated MEDLINE and Embase app search strategies used for the ‘MIB214 myCOPD app’ NICE topic. NNR is the number of references screened to find each relevant reference.ResultsThe MEDLINE and Embase filters achieved 98.6 percent and 98.5 percent recall against the validation set, respectively. In the case study they achieved 100 percent recall, reducing NNR from 348 to 147 in MEDLINE and from 456 to 271 in Embase.ConclusionsThe novel NICE health apps search filters retrieve evidence on apps from MEDLINE and Embase effectively and more efficiently than previous non-validated search strategies used at NICE.

The NICE OECD countries’ geographic search filters: Part 2—validation of the MEDLINE and Embase (Ovid) filters

Objective: We previously developed draft MEDLINE and Embase (Ovid) geographic search filters for Organisation for Economic Co-operation and Development (OECD) countries to assess their feasibility for finding evidence about the countries. Here, we describe the validation of these search filters.Methods: We identified OECD country references from thirty National Institute for Health and Care Excellence (NICE) guidelines to generate gold standard sets for MEDLINE (n=2,065) and Embase (n=2,023). We validated the filters by calculating their recall against these sets. We then applied the filters to existing search strategies for three OECD-focused NICE guideline reviews (NG103 on flu vaccination, NG140 on abortion care, and NG146 on workplace health) to calculate the filters’ impact on the number needed to read (NNR) of the searches.Results: The filters both achieved 99.95% recall against the gold standard sets. Both filters achieved 100% recall for the three NICE guideline reviews. The MEDLINE filter reduced NNR from 256 to 232 for the NG103 review, from 38 to 27 for the NG140 review, and from 631 to 591 for the NG146 review. The Embase filter reduced NNR from 373 to 341 for the NG103 review, from 101 to 76 for the NG140 review, and from 989 to 925 for the NG146 review.Conclusion: The NICE OECD countries’ search filters are the first validated filters for the countries. They can save time for research topics about OECD countries by finding the majority of evidence about OECD countries while reducing search result volumes in comparison to no filter use.

Translation and validation of PubMed and Embase search filters for identification of systematic reviews, intervention studies, and observational studies in the field of first aid

Objective: The aim of this project was to validate search filters for systematic reviews, intervention studies, and observational studies translated from Ovid MEDLINE and Embase syntax and used for searches in PubMed and Embase.com during the development of evidence summaries supporting first aid guidelines. We aimed to achieve a balance among recall, specificity, precision, and number needed to read (NNR).Methods: Reference gold standards were constructed per study type derived from existing evidence summaries. Search filter performance was assessed through retrospective searches and measurement of relative recall, specificity, precision, and NNR when using the translated search filters. Where necessary, search filters were optimized. Adapted filters were validated in separate validation gold standards.Results: Search filters for systematic reviews and observational studies reached recall of ≥85% in both PubMed and Embase. Corresponding specificities for systematic review filters were ≥96% in both databases, with a precision of 9.7% (NNR 10) in PubMed and 5.4% (NNR 19) in Embase. For observational study filters, specificity, precision, and NNR were 68%, 2%, and 51 in PubMed and 47%, 0.8%, and 123 in Embase, respectively. These filters were considered sufficiently effective. Search filters for intervention studies reached a recall of 85% and 83% in PubMed and Embase, respectively. Optimization led to recall of ≥95% with specificity, precision, and NNR of 49%, 1.3%, and 79 in PubMed and 56%, 0.74%, and 136 in Embase, respectively.Conclusions: We report validated filters to search for systematic reviews, observational studies, and intervention studies in guideline projects in PubMed and Embase.com.

Supply chain management: an integrative review from the agency theory perspective

PurposeThe purpose of the article is to demonstrate how agency theory has been used to address the dynamics involved in supply chain management. It is also dedicated to suggesting an agenda for future research.Design/methodology/approachWe performed an integrative literature review, based on the process detailed by Botelho et al. (2011), with search filters. The articles were obtained from the Scopus and Web of Science databases using the keywords “supply chain” and “agency theory”, with a subsequent analytical filter for “management”. The search initially identified 205 articles. After two screenings, 56 articles were selected for analysis.FindingsDespite attempts to infer the importance of research on agency theory in supply chain management, its application to the discipline is scarce. Clearly, agency theory provides valuable insights into the relationships in the supply chain. In the studies analyzed, the dynamics of performance, risk, sustainability, dyadic and inter-firm relationships, and supplier management are predominant.Originality/valueWhen considering unwanted behaviors throughout the supply chain, agency theory fills the explanatory gaps for these facts. It also proves to be a useful tool to answer mainly the dilemmas of underlying theories, such as transaction cost theory, resource-based view and network theory. Rare are the studies that examine the current state of the application of agency theory in the supply chain literature in the management field.

Laboratory animals search filter for different literature databases: PubMed, Embase, Web of Science and PsycINFO

Systematic reviews are important tools in animal research, but the ever-increasing number of studies makes retrieval of all relevant publications challenging. Search filters aid in retrieving as many animal studies as possible. In this paper we provide updated and expanded versions of the SYRCLE animal filters for PubMed and Embase. We provide the Embase filter for both Embase.com and via Ovid. Furthermore, we provide new animal search filters for Web of Science (WoS) and APA PsycINFO via psycnet.apa.org and via Ovid. Compared with previous versions, the new filters retrieved 0.5–47.1% (19 references for PubMed, 837 for WoS) more references in a real-life example. All filters retrieved additional references, comprising multiple relevant reviews. A random sample from WoS found at least one potentially relevant primary study. These animal search filters facilitate identifying as many animal studies as possible while minimising the number of non-animal studies.

Mental health during the COVID-19 pandemic

This study aimed to investigate the impacts of social distancing on the occurrence of symptoms of anxiety and depression reported in scientific production available in 2020, describing and analyzing the main triggering factors of mental health problems/diseases in the period of social distancing during the COVID-19 pandemic. The method used was an integrative literature review, with searches in Lilacs, SciELO, Medline, and PubMed databases. The following descriptors were used for the selection of publications: Social Distancing, Anxiety, and Depression, used in combination in the search strategy. To refine the search, filters were used: full text; Language Portuguese, English, and Spanish; Main subject; Type of document, with an article as the only type of literature accepted; Year of Publication 2020. This research identified 37 studies later categorized into five main themes: Physical Inactivity, Reduction of social contact and face-to-face interactions, Financial concerns and economic vulnerability, Loneliness, and Alcohol Consumption. The major impacts of social distancing on mental health were the drastic change in routine, favoring sedentary behavior, and limitation in interpersonal contact indicated in many studies as a generator of a high prevalence of harmful psychological effects, especially depression, anxiety, irritability, and irritability episodes of insomnia. The perception of loneliness as a consequence of the period of social distancing was also identified by most studies as associated with anxious and depressive outcomes and with an increased risk of suicidal ideation, as well as the increased use of alcohol, widely used as an escape from reality in the current context of economic resection, unemployment, indebtedness, and death of family and friends by COVID-19.

Auto Machine Learning Approaches to Refine the Identification of High-Quality Clinical Research Articles from The Biomedical Literature: A Study Protocol (Preprint)

UNSTRUCTURED Due to the continued rapid growth in published biomedical literature, it is increasingly difficult to identify and retrieve high-quality evidence. Machine learning approaches have been applied to address this issue. Some models developed using supervised machine learning approaches have achieved high sensitivity or recall, however precision has been variable. In a series of experiments, we will assess the performance of machine learning models to retrieve high-quality, high relevance evidence for clinical consideration from the biomedical literature. The models will be trained using an automated approach applied to a database of almost 100, 000 articles that have been tagged by highly trained research staff based on criteria for high-quality and assessed for clinical relevance by clinicians. We will evaluate and report on the effects of various classifiers, preprocessing steps, feature selection, and the use of balanced vs unbalanced datasets applied during model development on the performance of the derived supervised machine learning models. The series was devised to improve the precision of the retrieval of high-quality articles by applying a machine learning classifier sequentially after using high sensitivity Boolean search filters to an ongoing literature surveillance process. Our multi-level analysis of the various steps of machine learning model development will help expand the existing knowledge base on the effect of each step on the performance of machine learning models.

Models of virtual library users’ behavior analysis

In this paper, we present models for the analysis of the behavior of the virtual library (VL) users. Unlike the models presented in the literature, they use only the big data that is stored in the log files of virtual library servers and methods of statistics, association rules, and recommendation systems. The proposed models were implemented with R software. Using the proposed models, the analysis of the behavior of VL users of Lithuanian research and study of higher education institutions was performed for the first time. The results showed that the proposed models allow to operatively analyze the behavior of virtual library users using advanced search filters, facets, and provide suggestions for improvement of service quality.