Journals of Universitas Sangga Buana
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Patient Navigation for Gynecologic Cancer Care Continuum
No full textOur scoping review aims to assess how and to what effect has patient navigation (PN) been implemented across the cancer care continuum for breast and gynecologic cancer patient
Incorporation of Artificial Intelligence into Nursing Research: A JBI Scoping Review Protocol
No full textThis scoping review protocol explores the intersection of artificial intelligence (AI) and nursing research. Despite AI's potential for improving healthcare, its applications in nursing research are relatively uncharted, prompting the need to explore its benefits and challenges in this field. This review aims to map existing literature on the topic, identify key concepts, evidence sources, and gaps in research, and address the question: "How has AI been incorporated into nursing research, and what are the associated challenges and benefits?"
The methodology aligns with the Joanna Briggs Institute (JBI) guidelines for scoping reviews and the Preferred Reporting Items for Systematic Reviews for Scoping Reviews (PRISMA-ScR). Eligibility criteria are outlined, with focus placed on AI incorporation into nursing research by nursing researchers, educators, practitioners, and policymakers, in any context or institution.
A three-phase search strategy will be deployed, encompassing Initial Identification (exploration at CINAHL), Comprehensive Search (application of keywords across all relevant databases), and Reference List Review (examination of reference lists). The identified resources will be assessed and selected for review through the Endnote software and JBI SUMARI, by two independent reviewers. Disagreements will be resolved through discussion or third-party consultation.
Data extraction will be performed using JBI SUMARI’s standardized data extraction tool, with modifications made as necessary. Missing data will be sought from original authors. The data analysis will be carried out through basic inductive qualitative content analysis and coded data will be organized into categories. Findings will be presented through tables, visuals, and narrative descriptions to provide a comprehensive overview of AI incorporation into nursing research
Effectiveness and impact of bridging programs for first-generation students
No full textThis research project includes a randomized field experiment to test the effectiveness of two bridging programs, and a study into the processes of successful transition for 1st generation student
Have you seen Sandra Ciesek’s cat on Twitter? Self-Disclosure as Digital Challenges for Trust in Scientists on Social Media
No full textSurveys from the Science Barometer indicate that public trust in science, which had reached peak levels due to the pandemic, is gradually declining again (Weißkopf et al., 2022). Trust in science has been a measure of the impact of the pandemic (Bromme, 2022) and has illustrated the value of direct communication between scientists and the public (Szczuka et al., 2021).
However, the isolation of scientists* in the "ivory tower" (Bauer & Jensen,2011) and the focus on defending scientific knowledge rather than building relationships with the public (Dudo & Besley, 2016), along with stereotype-reinforcing portrayals in the media, have likely contributed to both ambivalent public perceptions and more negative stereotypes about scientists*.
Since affective competencies (such as integrity and benevolence) tend not to be attributed to the stereotypical image of researchers* relevant to trust in science, in contrast to competency-related characteristics (professional knowledge, expertise) (Weißkopf et al., 2022 Buldu, 2006; Finson,2002), Twitter as a tool for individualized and personalized media use could change the perception of scientists*. Tweets can be seen as a form of self-disclosure that, whether intentional or not, can influence users* perceptions (Utz, 2015).
It has already been shown that selfies of scientists* on social media can counteract negative stereotypical perceptions (Jarreau et al., 2019). Findings such as these already suggest the potential of using social media as a platform for self-disclosure and could lead to the attribution of affective competencies (integrity and benevolence) (Buldu, 2006; Finson, 2002).
The present study examines the extent to which scientists* personal and professional self-disclosure (adapted from Kim &Song, 2016) on Twitter affects trust in science (specifically, epistemic trust (Hendriks et al., 2015, 2016; Origgi, 2014; McAllister, 1995) and factual credibility regarding scientific findings (Appelman & Sunder, 2016; Metzger et al., 2003)). For this purpose, affective and competence-related perceptions of scientists* are considered in a differentiated manner and stereotypes of scientists* (Fiske & Dupree, 2014; Carli et al., 2016) are taken into account.
References
Appelman, A., & Sundar, S. S. (2016). Measuring message credibility: Construction and
validation of an exclusive scale. Journalism and Mass Communication Quarterly,
93(1), 59-79. https://doi.org/10.1177/1077699015606057
Bauer, M. W., & Jensen, P. (2011) The mobilization of scientists for public engagement.
Public Understanding of Science. 20(1):3–11.
Bromme, R. (2022) Informiertes Vertrauen in Wissenschaft: Lehren aus der COVID-19
Pandemie für das Verständnis naturwissenschaftlicher Grundbildung (scientific literacy).
Unterrichtswiss 50, 331–345. https://doi.org/10.1007/s42010-022-
00159-6
Buldu, M. (2006). Young children’s perceptions of scientists: A preliminary study. Educational
Research, 48(1), 121–132.
https://doi.org/10.1080/00131880500498602
Carli, L. L., Alawa, L., Lee, Y., Zhao, B., & Kim, E. (2016). Stereotypes about gender and
science: Women ≠ scientists. Psychology of Women Quarterly, 40(2), 244–
260. https://doi.org/10.1177/0361684315622645
Dudo A., & Besley, J.C. (2016). Scientists’ Prioritization of Communication Objectives for
Public Engagement. PLOS ONE, 11(2). https://doi.org/10.1371/journal.
pone.0148867
Finson, K. D. (2002), Drawing a Scientist: What We Do and Do Not Know After Fifty
Years of Drawings. School Science and Mathematics, 102: 335-345.
https://doi.org/10.1111/j.1949-8594.2002.tb18217.x
Fiske, S. T., & Dupree, C. (2014). Gaining trust as well as respect in communicating to
motivated audiences about science topics. Proceedings of the National Academy
of Sciences, 111(4), 13593–13597. https://doi.org/10.1073/pnas.1317505111
Hendriks, F., Kienhues, D., & Bromme, R. (2015). Measuring laypeople’s trust inexperts
in a digital age: The Muenster Epistemic Trustworthiness Inventory (METI). PLoS
ONE, 10 (10). doi:10.1371/journal.pone.0139309.
Hendriks, F., Kienhues, D., & Bromme, R. (2016). Trust in science and the science of
trust. In B. Blöbaum (Ed.), Trust and communication in a digitized world: Models
and concepts of trust research (pp. 143–159). Springer International Publishing/
Springer Nature. https://doi.org/10.1007/978-3-319-28059-2_8
Jarreau, P. B., Cancellare, I. A., Carmichael, B. J., Porter, L., Toker, D., & Yammine, S.
Z. (2019). Using selfies to challenge public stereotypes of scientists. PLOS ONE,
14(4), Article e0216625. https://doi.org/10.1371/journal.pone.0216625
Kim, J., & Song, H. (2016) Celebrity’s self-disclosure on Twitter and parasocial relationships:
a mediating role of social presence. Computers in Human Behavior 62: 570–
577.
McAllister, D. J. (1995). Affect- and cognition-based trust as foundations for interpersonal
cooperation in organizations. Academy of Management Journal, 38(1), 24–
59. https://doi.org/10.2307/256727
Metzger, M.J., Flanagin, A.J., Eyal, K., Lemus, D.R., & McCann, R.M. (2003). Credibility
for the 21st Century: Integrating Perspectives on Source, Message, and Media
Credibility in the Contemporary Media Environment. Annals of the International
Communication Association, 27, 293 - 335.
Origgi, G. (2014). Epistemic trust. In: Capet P, Delavallade T, editors. Information Evaluation.
1st ed. London: Wiley-ISTE (p. 35–54).
Szczuka, J. M., Meinert, J., & Krämer, N. (2020). Listen to the Scientists: effects of exposure to
scientists and general media consumption on cognitive, affective and behavioral mechanisms
during the COVID-19 pandemic. PsyArXiv. https://doi.org/10.31234/osf.io/6j8qd
Utz, S. (2015). The function of self-disclosure on social network sites: Not only intimate, but also
positive and entertaining self-disclosures increase the feeling of connection. Computers in Human
Behavior, 45, 1–10. https://doi.org/10.1016/j.chb.2014.11.076
Weißkopf, M., Ziegler, R., & Kremer, B. (2022). Science Barometer 2021. GESIS, Cologne. ZA7640 Data file Version 1.0.0, https://doi.org/10.4232/1.1385
Speed-Accuracy Tradeoff for Flanker Task - Midpoint Violation Exploration
No full textData and analysis scrip
Many Labs 4: Replicating Mortality Salience with and without Original Author Involvement
No full textWe conduct replications of the same TMT study across 17 labs, half with detailed advice from original authors, half without any help whatsoever
