TUScholarShare

TUScholarShare

TUScholarShare is a service to support the needs of the Temple University community around sharing, promoting, and archiving the wide range of scholarly works created in the course of research and teaching. The repository aims to make Temple scholarship freely available online to a global audience, with the goal of advancing knowledge and learning.

 

                                                   

 

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  • Performing Spirometry and Low Dose Chest CT to Screen for COPD Systematic Review Search Strategy

    Dachert, Stephen; Siego, Michaela; Nace, Travis; Criner, Gerard J. (2024-02-20)
    To identify studies to include or consider for this systematic review, the review team worked with a librarian (TN) to develop detailed search strategies for each database. The PRISMA-S extension was followed for search reporting. The librarian (TN) developed the search for PubMed (NLM) and translated the search for every database searched. The PubMed (NLM) search strategy was reviewed by the research team to check for accuracy and term relevancy. All final searches were peer-reviewed by another librarian (Jacob Brintzenhoff) following the Peer Review of Electronic Search Strategies (PRESS checklist). The databases included in this search are PubMed (NLM), Embase (Elsevier), Cochrane Central (Wiley), CINAHL (EbscoHost), and Web of Science (Clarivate Analytics), using a combination of keywords and subject headings. A grey literature search included two clinical trials registries, clinicaltrials.gov and WHO ICTRP (https://trialsearch.who.int/), and the pre-print MedRxiv (https://www.medrxiv.org/). The search was limited to English language and to adults. All final searches were performed on November 17, 2023 by the librarian (TN) and were fully reported on November 21, 2023. The full search strategies as reported by the librarian are provided in Appendix(___). They are also archived at [DOI].
  • Transforming the Knowledge Commons: Faculty-Librarian Collaborations that Advance Open Educational Practices, Student Agency, and Equity

    De Voe, Kristina; De Voe|0000-0003-1590-3379 (2023-06-25)
    Open educational practices (OEP) focus on open teaching and open content, offering students opportunities to do purposeful work that is available to a public beyond the classroom. Students can “contribute to the knowledge commons, not just consume it, in meaningful and lasting ways…shap[ing] the world as they encounter it” (DeRosa and Jhangiani, 2017). As active agents in their own learning, students need a community with which to explore their information privilege, test and contest ideas, and create meaning. Wikipedia provides students an authentic public community with which to participate. It also provides an outlet for publishing information on topics that are underrepresented or misrepresented in traditional publishing and by mainstream media, allowing students to see scholarship creation as part of an ongoing conversation rather than an end product. Wikipedia-editing permits diverse stories, histories, and contributors to become visible while promoting creative expression, connection, and collaboration among students. This poster is informed by a faculty-librarian collaboration that entailed developing scaffolded, renewable assignments involving Wikipedia across five years and two undergraduate Media Studies classes. Foundational knowledge of what OEP are, the characteristics of renewable assignments, and the infrastructure of Wikipedia’s platform will be covered. Data gathered from WikiEdu class dashboards and library edit-a-thons, as well as questions and student feedback from debriefing sessions, will be included in the poster. Finally, strategies for designing effective assignments involving Wikipedia-editing will also be offered as well as ideas for how librarians can best support faculty and students engaged in these activities.
  • Socio-Clinical Correlations with Threat Perception and Self-Efficacy in People with Type 2 Diabetes

    Hu, Jianli; Bass, Sarah; Swavely, Deborah; Zisman-Ilani, Yaara; Chen, Sophia K.; Kim, Sarah; Kelly, Patrick J.A.; Hoadley, Ariel; Rubin, Daniel; Bass|0000-0003-2742-1609; Zisman-Ilani|0000-0001-6852-2583; Hoadley|0000-0003-1360-0358; Rubin|0000-0002-6871-6246; Hu|0000-0002-3022-1950 (2024-01-26)
    Introduction: Medically underserved people perform suboptimal type 2 diabetes (T2D) self-care, which contributes to worse diabetes control and higher complication rates. A better understanding of how beliefs about self-efficacy and the threat of diabetic complications affect self-management behavior may be informative to develop more effective interventions. Research Design and Methods: The Extended Parallel Processing Model (EPPM), a theoretical framework of perceived efficacy and disease threat, was used in a cross-sectional study to categorize 168 adults with T2D from urban safety-net clinics and the local community by self-efficacy and perceived threat from T2D and cardiovascular disease. The EPPM model offers four categories: high threat (HT)/high efficacy (HE), low threat (LT)/low efficacy (LE), HT/LE, and LT/HE. Participant demographic information, complications, medications, and other characteristics were compared across the EPPM groups. Results: The sample included 168 participants, of which 76% were Black, 16% were Hispanic, and 7% were White. HT/LE people had the lowest medication adherence (p<0.01), the lowest T2D management score (p<0.0001), the highest A1C numerically (p=0.10), and the most microvascular complications relative to other EPPM groups (p<0.01). Gender, Race/Ethnicity, education, and health insurance did not vary among EPPM groups. Conclusions: The EPPM is associated with T2D clinical outcomes and self-management behaviors. Moving people from HT/LE to LT/HE may improve T2D management. This model may be useful to target people with T2D for behavioral intervention.
  • Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference

    Li, Bangzheng; Yin, Wenpeng; Chen, Muhao (2022-05-16)
    The task of ultra-fine entity typing (UFET) seeks to predict diverse and free-form words or phrases that describe the appropriate types of entities mentioned in sentences. A key challenge for this task lies in the large number of types and the scarcity of annotated data per type. Existing systems formulate the task as a multi-way classification problem and train directly or distantly supervised classifiers. This causes two issues: (i) the classifiers do not capture the type semantics because types are often converted into indices; (ii) systems developed in this way are limited to predicting within a pre-defined type set, and often fall short of generalizing to types that are rarely seen or unseen in training. This work presents LITE🍻, a new approach that formulates entity typing as a natural language inference (NLI) problem, making use of (i) the indirect supervision from NLI to infer type information meaningfully represented as textual hypotheses and alleviate the data scarcity issue, as well as (ii) a learning-to-rank objective to avoid the pre-defining of a type set. Experiments show that, with limited training data, LITE obtains state-of-the-art performance on the UFET task. In addition, LITE demonstrates its strong generalizability by not only yielding best results on other fine-grained entity typing benchmarks, more importantly, a pre-trained LITE system works well on new data containing unseen types.
  • Online interventions for reducing hate speech and cyberhate: A systematic review

    Windisch, Steven; Wiedlitzka, Susann; Olaghere, Ajima; Jenaway, Elizabeth (2022-05-25)
    Background: The unique feature of the Internet is that individual negative attitudes toward minoritized and racialized groups and more extreme, hateful ideologies can find their way onto specific platforms and instantly connect people sharing similar prejudices. The enormous frequency of hate speech/cyberhate within online environments creates a sense of normalcy about hatred and the potential for acts of intergroup violence or political radicalization. While there is some evidence of effective interventions to counter hate speech through television, radio, youth conferences, and text messaging campaigns, interventions for online hate speech have only recently emerged. Objectives: This review aimed to assess the effects of online interventions to reduce online hate speech/cyberhate. Search Methods: We systematically searched 2 database aggregators, 36 individual databases, 6 individual journals, and 34 websites, as well as bibliographies of published reviews of related literature, and scrutiny of annotated bibliographies of related literature. Inclusion Criteria: We included randomized and rigorous quasi-experimental studies of online hate speech/cyberhate interventions that measured the creation and/or consumption of hateful content online and included a control group. Eligible populations included youth (10–17 years) and adult (18+ years) participants of any racial/ethnic background, religious affiliation, gender identity, sexual orientation, nationality, or citizenship status. Data Collection and Analysis: The systematic search covered January 1, 1990 to December 31, 2020, with searches conducted between August 19, 2020 and December 31, 2020, and supplementary searches undertaken between March 17 and 24, 2022. We coded characteristics of the intervention, sample, outcomes, and research methods. We extracted quantitative findings in the form of a standardized mean difference effect size. We computed a meta-analysis on two independent effect sizes. Main Results: Two studies were included in the meta-analysis, one of which had three treatment arms. For the purposes of the meta-analysis we chose the treatment arm from the Álvarez-Benjumea and Winter (2018) study that most closely aligned with the treatment condition in the Bodine-Baron et al. (2020) study. However, we also present additional single effect sizes for the other treatment arms from the Álvarez-Benjumea and Winter (2018) study. Both studies evaluated the effectiveness of an online intervention for reducing online hate speech/cyberhate. The Bodine-Baron et al. (2020) study had a sample size of 1570 subjects, while the Álvarez-Benjumea and Winter (2018) study had a sample size of 1469 tweets (nested in 180 subjects). The mean effect was small (g = −0.134, 95% confidence interval [−0.321, −0.054]). Each study was assessed for risk of bias on the following domains: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported results. Both studies were rated as “low risk” on the randomization process, deviations from intended interventions, and measurement of the outcome domains. We assessed the Bodine-Baron et al. (2020) study as “some” risk of bias regarding missing outcome data and “high risk” for selective outcome reporting bias. The Álvarez-Benjumea and Winter (2018) study was rated as “some concern” for the selective outcome reporting bias domain. Authors' Conclusions: The evidence is insufficient to determine the effectiveness of online hate speech/cyberhate interventions for reducing the creation and/or consumption of hateful content online. Gaps in the evaluation literature include the lack of experimental (random assignment) and quasi-experimental evaluations of online hate speech/cyberhate interventions, addressing the creation and/or consumption of hate speech as opposed to the accuracy of detection/classification software, and assessing heterogeneity among subjects by including both extremist and non-extremist individuals in future intervention studies. We provide suggestions for how future research on online hate speech/cyberhate interventions can fill these gaps moving forward.

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