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            "creatorSummary": "Espelage and Hong",
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            "title": "Cyberbullying Prevention and Intervention Efforts: Current Knowledge and Future Directions",
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                    "creatorType": "author",
                    "firstName": "Dorothy L.",
                    "lastName": "Espelage"
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                {
                    "creatorType": "author",
                    "firstName": "Jun Sung",
                    "lastName": "Hong"
                }
            ],
            "abstractNote": "Bullying is a serious public health concern that is associated with significant negative mental, social, and physical outcomes. Technological advances have increased adolescents’ use of social media, and online communication platforms have exposed adolescents to another mode of bullying—cyberbullying. Prevention and intervention materials, from websites and tip sheets to classroom curriculum, have been developed to help youth, parents, and teachers address cyberbullying. While youth and parents are willing to disclose their experiences with bullying to their health care providers, these disclosures need to be taken seriously and handled in a caring manner. Health care providers need to include questions about bullying on intake forms to encourage these disclosures. The aim of this article is to examine the current status of cyberbullying prevention and intervention. Research support for several school-based intervention programs is summarised. Recommendations for future research are provided.",
            "publicationTitle": "The Canadian Journal of Psychiatry",
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            "pages": "374-380",
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            "journalAbbreviation": "Can J Psychiatry",
            "DOI": "10/d876",
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            "url": "https://doi.org/10.1177/0706743716684793",
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            "shortTitle": "Cyberbullying Prevention and Intervention Efforts",
            "language": "en",
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                    "tag": "electronic aggression",
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            "creatorSummary": "Yacchirema et al.",
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        "data": {
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            "version": 463,
            "itemType": "journalArticle",
            "title": "Fall detection system for elderly people using IoT and ensemble machine learning algorithm",
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                    "creatorType": "author",
                    "firstName": "Diana",
                    "lastName": "Yacchirema"
                },
                {
                    "creatorType": "author",
                    "firstName": "Jara Suárez",
                    "lastName": "de Puga"
                },
                {
                    "creatorType": "author",
                    "firstName": "Carlos",
                    "lastName": "Palau"
                },
                {
                    "creatorType": "author",
                    "firstName": "Manuel",
                    "lastName": "Esteve"
                }
            ],
            "abstractNote": "Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.",
            "publicationTitle": "Personal and Ubiquitous Computing",
            "publisher": "",
            "place": "",
            "date": "2019-11-01",
            "volume": "23",
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            "pages": "801-817",
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            "PMCID": "",
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            "shortTitle": "",
            "language": "en",
            "libraryCatalog": "Springer Link",
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    {
        "key": "4A3FI4BZ",
        "version": 463,
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            "creatorSummary": "Til et al.",
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        "data": {
            "key": "4A3FI4BZ",
            "version": 463,
            "itemType": "journalArticle",
            "title": "A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Kaela Van",
                    "lastName": "Til"
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                {
                    "creatorType": "author",
                    "firstName": "Melvin G.",
                    "lastName": "McInnis"
                },
                {
                    "creatorType": "author",
                    "firstName": "Amy",
                    "lastName": "Cochran"
                }
            ],
            "abstractNote": "Objectives Self-monitoring is recommended for individuals with bipolar disorder, with numerous technological solutions available. This study aimed to identify basic components of these solutions that increase engagement with self-monitoring. Methods Participants with bipolar disorder (n = 47) monitored their symptoms with a Fitbit and a smartphone app and were randomly assigned to either review or not review recorded symptoms weekly. We tested whether individuals would better adhere to and prefer monitoring with passive monitoring with an activity tracker compared to active monitoring with a smartphone app and whether individuals would better adhere to self-monitoring if their recorded symptoms were reviewed with an interviewer. Results Monitoring with a smartphone app achieved similar adherence and preference to Fitbit (P > .85). Linear mixed effects modeling found adherence decreased significantly more over the study for the Fitbit (12% more, P < .001) even though more participants reported they would use the Fitbit over a year compared to the app (72.3% vs 46.8%). Reviewing symptoms weekly did not improve adherence, but most participants reported they would prefer to review symptoms with a clinician (74.5%) and on monthly basis (57.5%) compared to alternatives. Participants endorsed sleep as the most important symptom to monitor, forgetfulness as the largest barrier to self-monitoring, and raising self-awareness as the best reason for self-monitoring. Conclusions We recommend a combined strategy of wearable and mobile monitoring that includes reminders, targets raising self-awareness, and tracks sleep. A clinician may want to review symptoms on a monthly basis. Trial registration: ClinicalTrials.gov NCT03358238.",
            "publicationTitle": "Bipolar Disorders",
            "publisher": "",
            "place": "",
            "date": "2020",
            "volume": "22",
            "issue": "2",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "182-190",
            "series": "",
            "seriesTitle": "",
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            "journalAbbreviation": "",
            "DOI": "10/gmmrkj",
            "citationKey": "",
            "url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/bdi.12849",
            "accessDate": "2021-04-24T21:41:39Z",
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            "PMCID": "",
            "ISSN": "1399-5618",
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            "shortTitle": "",
            "language": "en",
            "libraryCatalog": "Wiley Online Library",
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            "rights": "© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd",
            "extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/bdi.12849",
            "tags": [
                {
                    "tag": "Bipolar disorder",
                    "type": 1
                },
                {
                    "tag": "Fitness Trackers",
                    "type": 1
                },
                {
                    "tag": "Monitoring",
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                },
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                    "tag": "Physiologic",
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                },
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                    "tag": "Smartphone",
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                }
            ],
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            "dateAdded": "2021-04-24T21:41:39Z",
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            "creatorSummary": "Garcia-Ceja et al.",
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            "itemType": "journalArticle",
            "title": "Mental health monitoring with multimodal sensing and machine learning: A survey",
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                {
                    "creatorType": "author",
                    "firstName": "Enrique",
                    "lastName": "Garcia-Ceja"
                },
                {
                    "creatorType": "author",
                    "firstName": "Michael",
                    "lastName": "Riegler"
                },
                {
                    "creatorType": "author",
                    "firstName": "Tine",
                    "lastName": "Nordgreen"
                },
                {
                    "creatorType": "author",
                    "firstName": "Petter",
                    "lastName": "Jakobsen"
                },
                {
                    "creatorType": "author",
                    "firstName": "Ketil J.",
                    "lastName": "Oedegaard"
                },
                {
                    "creatorType": "author",
                    "firstName": "Jim",
                    "lastName": "Tørresen"
                }
            ],
            "abstractNote": "Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner. Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc. Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning. We focused on research works about mental disorders/conditions such as: depression, anxiety, bipolar disorder, stress, etc. We propose a classification taxonomy to guide the review of related works and present the overall phases of MHMS. Moreover, research challenges in the field and future opportunities are also discussed.",
            "publicationTitle": "Pervasive and Mobile Computing",
            "publisher": "",
            "place": "",
            "date": "December 1, 2018",
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            "partNumber": "",
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            "pages": "1-26",
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            "seriesTitle": "",
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            "journalAbbreviation": "Pervasive and Mobile Computing",
            "DOI": "10/gfp98s",
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            "url": "https://www.sciencedirect.com/science/article/pii/S1574119217305692",
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            "PMCID": "",
            "ISSN": "1574-1192",
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            "shortTitle": "Mental health monitoring with multimodal sensing and machine learning",
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                    "tag": "Mental disorders",
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                    "type": 1
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    {
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            "creatorSummary": "Rice and Barman-Adhikari",
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        "data": {
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            "version": 463,
            "itemType": "journalArticle",
            "title": "Internet and Social Media Use as a Resource Among Homeless Youth*",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Eric",
                    "lastName": "Rice"
                },
                {
                    "creatorType": "author",
                    "firstName": "Anamika",
                    "lastName": "Barman-Adhikari"
                }
            ],
            "abstractNote": "Little is known about internet and social media use among homeless youth. Consistent with typologies prevalent among housed youth, we found that homeless youth were using internet and social media for entertainment, sociability, and instrumental purposes. Using Haythornwaite's (2001) premise that it is important to look at the types of ties accessed in understanding the impact of new media, we found that homeless youth were predominantly using e-mail to reach out to their parents, caseworkers, and potential employers, while, using social media to communicate with their peers. Using the “Social Capital” perspective, we found that youth who were connecting to maintained or bridging social ties were more likely to look for jobs and housing online than youth who did not.",
            "publicationTitle": "Journal of Computer-Mediated Communication",
            "publisher": "",
            "place": "",
            "date": "January 1, 2014",
            "volume": "19",
            "issue": "2",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "232-247",
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            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "Journal of Computer-Mediated Communication",
            "DOI": "10/f5pvw4",
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            "url": "https://doi.org/10.1111/jcc4.12038",
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            "PMCID": "",
            "ISSN": "1083-6101",
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            "shortTitle": "",
            "language": "",
            "libraryCatalog": "Silverchair",
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            "creatorSummary": "Dilgul et al.",
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            "version": 463,
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            "title": "Virtual reality group therapy for the treatment of depression: A qualitative study on stakeholder perspectives",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "M.",
                    "lastName": "Dilgul"
                },
                {
                    "creatorType": "author",
                    "firstName": "L. M.",
                    "lastName": "Hickling"
                },
                {
                    "creatorType": "author",
                    "firstName": "D.",
                    "lastName": "Antonie"
                },
                {
                    "creatorType": "author",
                    "firstName": "S.",
                    "lastName": "Priebe"
                },
                {
                    "creatorType": "author",
                    "firstName": "V. J.",
                    "lastName": "Bird"
                }
            ],
            "abstractNote": "Background: Cognitive behavioral group therapy alleviates depression by teaching patients to think and behave in more positive ways. Teletherapy (e.g., Zoom) is becoming more widely used, especially during the COVID-19 pandemic (where meeting in person is not safe). The current study explores the acceptability of taking teletherapy to the next level: Virtual Reality Group Therapy (VRGT). \n \nMethods: Semistructured interviews were conducted to explore stakeholder views on VRGT. Ten depressed patients and ten therapists watched a demonstration video of the proposed VRGT intervention and tested the VR application using a stand-alone VR headset. In VRGT, patients will use an avatar to interact with each other and with their therapist via networked multiparticipant VR. \n \nResults: Therapists and patients generally responded favorably to the idea of doing group therapy sessions in VR. Patients especially liked the idea of remaining anonymous via an avatar. Patients and therapists both indicated that the anonymity provided by avatars could increase patient’s willingness to make disclosures (to talk more freely and honestly), which could increase participation and could lead to better group cohesion. \n \nConclusion: Although the findings suggested that VRGT may be more acceptable for some patients than for others, overall, the response of the patients and therapists was largely positive. Recommendations from this study could be used during the COVID-19 pandemic to deliver VRGTs. Finally, design ideas for creating a group VR world custom-designed for group therapy are discussed.",
            "publicationTitle": "",
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            "journalAbbreviation": "",
            "DOI": "10/gmmrkf",
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            "url": "https://qmro.qmul.ac.uk/xmlui/handle/123456789/71248",
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            "ISSN": "2673-4192",
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            "shortTitle": "Virtual reality group therapy for the treatment of depression",
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            "libraryCatalog": "qmro.qmul.ac.uk",
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            "extra": "Accepted: 2021-04-13T16:11:33Z",
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                    "firstName": "John D.",
                    "lastName": "Clapp"
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                    "creatorType": "author",
                    "firstName": "Danielle R.",
                    "lastName": "Madden"
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                    "creatorType": "author",
                    "firstName": "Hugo Gonzalez",
                    "lastName": "Villasanti"
                },
                {
                    "creatorType": "author",
                    "firstName": "Luis Felipe",
                    "lastName": "Giraldo"
                },
                {
                    "creatorType": "author",
                    "firstName": "Kevin M.",
                    "lastName": "Passino"
                },
                {
                    "creatorType": "author",
                    "firstName": "Mark B.",
                    "lastName": "Reed"
                },
                {
                    "creatorType": "author",
                    "firstName": "Isabel Fernandez",
                    "lastName": "Puentes"
                }
            ],
            "abstractNote": "Drinking events are dynamic. The interactions of individuals, groups, and the environment as they relate to drinking behaviour are overwhelmingly complex. This paper presents an empirically grounded dynamic conceptual model to better understand drinking events. Using a collaborative mixed-methods approach, we developed an aggregated system dynamic model of drinking events. The process began with identification of system elements and boundaries. Once the first aspects of the model were completed, we constructed a causal loop diagram, an aggregated causal loop diagram, and stock and flow diagrams. Finally, we developed and ran computer simulations of the dynamical models. The model presented here can be used to guide future agent-based, system dynamics, or differential equation-based models. Such models can help inform future empirical work and modelling to increase the understanding of drinking events and provide solutions to the problems that happen proximal to these events. Copyright © 2018 John Wiley & Sons, Ltd.",
            "publicationTitle": "Systems Research and Behavioral Science",
            "publisher": "",
            "place": "",
            "date": "2018",
            "volume": "35",
            "issue": "3",
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            "partNumber": "",
            "partTitle": "",
            "pages": "265-281",
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            "journalAbbreviation": "",
            "DOI": "10/gdsfx6",
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            "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/sres.2478",
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            "shortTitle": "A System Dynamic Model of Drinking Events",
            "language": "en",
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            "callNumber": "",
            "rights": "Copyright © 2018 John Wiley & Sons, Ltd.",
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            "tags": [
                {
                    "tag": "conceptual models",
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                {
                    "tag": "drinking behaviour",
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                },
                {
                    "tag": "drinking events",
                    "type": 1
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            "creatorSummary": "North et al.",
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            "title": "A pilot study using mixed gps/narrative interview methods to understand geospatial behavior in homeless populations",
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                    "creatorType": "author",
                    "firstName": "Carol S.",
                    "lastName": "North"
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                {
                    "creatorType": "author",
                    "firstName": "Sarah E.",
                    "lastName": "Wohlford"
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                {
                    "creatorType": "author",
                    "firstName": "Denis J.",
                    "lastName": "Dean"
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                {
                    "creatorType": "author",
                    "firstName": "Melissa",
                    "lastName": "Black"
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                {
                    "creatorType": "author",
                    "firstName": "Margaret E.",
                    "lastName": "Balfour"
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                {
                    "creatorType": "author",
                    "firstName": "James C.",
                    "lastName": "Petrovich"
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                {
                    "creatorType": "author",
                    "firstName": "Dana L.",
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                    "creatorType": "author",
                    "firstName": "David E.",
                    "lastName": "Pollio"
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            ],
            "abstractNote": "Tracking the movements of homeless populations presents methodological difficulties, but understanding their movements in space and time is needed to inform optimal placement of services. This pilot study developed, tested, and refined methods to apply global positioning systems (GPS) technology paired with individual narratives to chronicle the movements of homeless populations. Detail of methods development and difficulties encountered and addressed, and geospatial findings are provided. A pilot sample of 29 adults was recruited from a low-demand homeless shelter in the downtown area of Fort Worth, Texas. Pre- and post-deployment interviews provided participant characteristics and planned and retrospectively-reported travels. Only one of the first eight deployments returned with sufficient usable data. Ultimately 19 participants returned the GPS device with >20 h of usable data. Protocol adjustments addressing methodological difficulties achieved 81 % of subsequent participants returning with sufficient usable data. This study established methods and demonstrated feasibility for tracking homeless population travels.",
            "publicationTitle": "Community Mental Health Journal",
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            "date": "2017-08-01",
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            "title": "Personalized medicine and cognitive behavioral therapies for depression: Small effects, big problems, and bigger data",
            "creators": [
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                    "firstName": "Lorenzo",
                    "lastName": "Lorenzo-Luaces"
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                    "creatorType": "author",
                    "firstName": "Allison",
                    "lastName": "Peipert"
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                    "creatorType": "author",
                    "firstName": "Robinson",
                    "lastName": "De Jesús Romero"
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                    "creatorType": "author",
                    "firstName": "Lauren A.",
                    "lastName": "Rutter"
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                    "creatorType": "author",
                    "firstName": "Natalie",
                    "lastName": "Rodriguez-Quintana"
                }
            ],
            "abstractNote": "Cognitive-behavioral therapies (CBTs) are the most widely studied form of psychotherapy for disorders like depression and anxiety. Nonetheless, there is heterogeneity in response to CBTs vs. other treatments. Researchers have become increasingly interested in using pre-treatment individual differences (i.e., moderators) to match patients to the most effective treatments for them. Several methods to combine multiple variables to create precision treatment rules (PTRs) that identify subgroups have been proposed. We review the rationale behind multivariable PTRs as well as the findings of studies that have used different PTRs. We identify conceptual and methodological issues in the literature. Multivariable treatment assignment is a promising avenue of research. Nonetheless, effect sizes appear to be small and most of the samples that have been used to study these questions have been grossly underpowered to detect small effects. We recommend researchers explore multivariable treatment selection strategies, particularly those resembling risk stratification, in heterogeneous samples of patients undergoing low-intensity CBTs vs. realistic minimal controls.",
            "publicationTitle": "International Journal of Cognitive Therapy",
            "publisher": "",
            "place": "",
            "date": "2021-03-01",
            "volume": "14",
            "issue": "1",
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            "partNumber": "",
            "partTitle": "",
            "pages": "59-85",
            "series": "",
            "seriesTitle": "",
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            "journalAbbreviation": "J Cogn Ther",
            "DOI": "10/gjw3vr",
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            "url": "https://doi.org/10.1007/s41811-020-00094-3",
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            "PMCID": "",
            "ISSN": "1937-1217",
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            "shortTitle": "Personalized medicine and cognitive behavioral therapies for depression",
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            "creatorSummary": "Huttar and BrintzenhofeSzoc",
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            "version": 481,
            "itemType": "journalArticle",
            "title": "Virtual Reality and Computer Simulation in Social Work Education: A Systematic Review",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Carol M.",
                    "lastName": "Huttar"
                },
                {
                    "creatorType": "author",
                    "firstName": "Karlynn",
                    "lastName": "BrintzenhofeSzoc"
                }
            ],
            "abstractNote": "The use of virtual reality and computer simulation are gaining ground in social work as viable teaching methods. Traditionally recognized as ancillary to classroom learning, they are being incorporated strategically into course curricula. A systematic review was undertaken to answer the questions, How are virtual reality and computer simulation technology being used to train social workers, and are they effective? Seven academic data sets and the Council on Social Work Education and the Society for Social Work and Research conference abstract databases were searched. Based on inclusion criteria, seven full articles were included revealing five themes. The technology is primarily used to teach direct practice rather than macro-level skills and is geared toward the education of students rather than practitioners.",
            "publicationTitle": "Journal of Social Work Education",
            "publisher": "Routledge",
            "place": "",
            "date": "January 2, 2020",
            "volume": "56",
            "issue": "1",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "131-141",
            "series": "",
            "seriesTitle": "",
            "seriesText": "",
            "journalAbbreviation": "",
            "DOI": "10/gmmrkc",
            "citationKey": "",
            "url": "https://doi.org/10.1080/10437797.2019.1648221",
            "accessDate": "2021-04-24T23:04:10Z",
            "PMID": "",
            "PMCID": "",
            "ISSN": "1043-7797",
            "archive": "",
            "archiveLocation": "",
            "shortTitle": "Virtual Reality and Computer Simulation in Social Work Education",
            "language": "",
            "libraryCatalog": "Taylor and Francis+NEJM",
            "callNumber": "",
            "rights": "",
            "extra": "_eprint: https://doi.org/10.1080/10437797.2019.1648221",
            "tags": [],
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            "dateAdded": "2021-04-24T23:04:10Z",
            "dateModified": "2021-08-29T04:01:14Z"
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    },
    {
        "key": "34CRYZXH",
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            "itemType": "journalArticle",
            "title": "Fitting the description: Historical and sociotechnical elements of facial recognition and anti-black surveillance",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Damien Patrick",
                    "lastName": "Williams"
                }
            ],
            "abstractNote": "It is increasingly evident that if researchers and policymakers want to meaningfully develop an understanding of responsible innovation, we must first ask whether some sociotechnical systems should be developed, at all. Here I argue that systems like facial recognition, predictive policing, and biometrics are predicated on myriad human prejudicial biases and assumptions which must be named and interrogated prior to any innovation. Further, the notions of individual responsibility inherent in discussions of technological ethics and fairness overburden marginalized peoples with a demand to prove the reality of their marginalization. Instead, we should focus on equity and justice, valuing the experiential knowledge of marginalized peoples and optimally positioning them to enact deep, lasting change. My position aligns with those in Science, Technology, and Society (STS) which center diverse and situated knowledges, and is articulated together with calls for considering within science and engineering wider sociocultural concerns like justice and equality.",
            "publicationTitle": "Journal of Responsible Innovation",
            "publisher": "Routledge",
            "place": "",
            "date": "December 4, 2020",
            "volume": "7",
            "issue": "sup1",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "74-83",
            "series": "",
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            "journalAbbreviation": "",
            "DOI": "10/gmmrkd",
            "citationKey": "",
            "url": "https://doi.org/10.1080/23299460.2020.1831365",
            "accessDate": "2021-04-24T22:43:57Z",
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            "PMCID": "",
            "ISSN": "2329-9460",
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            "shortTitle": "Fitting the description",
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            "callNumber": "",
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            "extra": "_eprint: https://doi.org/10.1080/23299460.2020.1831365",
            "tags": [
                {
                    "tag": "STS",
                    "type": 1
                },
                {
                    "tag": "algorithms",
                    "type": 1
                },
                {
                    "tag": "digital imaging",
                    "type": 1
                },
                {
                    "tag": "disability studies",
                    "type": 1
                },
                {
                    "tag": "race and gender studies",
                    "type": 1
                },
                {
                    "tag": "surveillance",
                    "type": 1
                }
            ],
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            "relations": {},
            "dateAdded": "2021-04-24T22:43:57Z",
            "dateModified": "2021-08-29T04:01:14Z"
        }
    },
    {
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        "version": 481,
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            "creatorSummary": "Turner Lee",
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        "data": {
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            "version": 481,
            "itemType": "journalArticle",
            "title": "Detecting racial bias in algorithms and machine learning",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Nicol",
                    "lastName": "Turner Lee"
                }
            ],
            "abstractNote": "Purpose The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries and public policies that can detect or reduce the likelihood of racial bias in algorithmic design and execution. Design/methodology/approach The paper shares examples in the US where algorithmic biases have been reported and the strategies for explaining and addressing them. Findings The findings of the paper suggest that explicit racial bias in algorithms can be mitigated by existing laws, including those governing housing, employment, and the extension of credit. Implicit, or unconscious, biases are harder to redress without more diverse workplaces and public policies that have an approach to bias detection and mitigation. Research limitations/implications The major implication of this research is that further research needs to be done. Increasing the scholarly research in this area will be a major contribution in understanding how emerging technologies are creating disparate and unfair treatment for certain populations. Practical implications The practical implications of the work point to areas within industries and the government that can tackle the question of algorithmic bias, fairness and accountability, especially African-Americans. Social implications The social implications are that emerging technologies are not devoid of societal influences that constantly define positions of power, values, and norms. Originality/value The paper joins a scarcity of existing research, especially in the area that intersects race and algorithmic development.",
            "publicationTitle": "Journal of Information, Communication and Ethics in Society",
            "publisher": "Emerald Publishing Limited",
            "place": "",
            "date": "2018-01-01",
            "volume": "16",
            "issue": "3",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "252-260",
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            "seriesTitle": "",
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            "journalAbbreviation": "",
            "DOI": "10/ghvdkd",
            "citationKey": "",
            "url": "https://doi.org/10.1108/JICES-06-2018-0056",
            "accessDate": "2021-04-24T23:01:23Z",
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            "PMCID": "",
            "ISSN": "1477-996X",
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            "shortTitle": "",
            "language": "",
            "libraryCatalog": "Emerald Insight",
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            "rights": "",
            "extra": "",
            "tags": [
                {
                    "tag": "Advertising",
                    "type": 1
                },
                {
                    "tag": "Artificial intelligence",
                    "type": 1
                },
                {
                    "tag": "Civil race relations",
                    "type": 1
                },
                {
                    "tag": "Civil society",
                    "type": 1
                },
                {
                    "tag": "Communication",
                    "type": 1
                },
                {
                    "tag": "Computer ethics",
                    "type": 1
                },
                {
                    "tag": "Race and political rights",
                    "type": 1
                }
            ],
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            "dateAdded": "2021-04-24T23:01:23Z",
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