CoChat
Sign Up
Cited by 20

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

A Peer-Led, Artificial Intelligence–Augmented Social Network Intervention to Prevent HIV Among Youth Experiencing Homelessness

Rice, Eric, Wilder, Bryan, Onasch-Vera, Laura, DiGuiseppi, Graham, Petering, Robin, Hill, Chyna, Yadav, Amulya, Lee, Sung-Jae, Tambe, Milind·2021·JAIDS Journal of Acquired Immune Deficiency Syndromes
DOI 10.1097/qai.0000000000002807PMC PMC8579989
Open this paper in CoChat — free account

Abstract

Background: Youth experiencing homelessness (YEH) are at elevated risk of HIV/AIDS and disproportionately identify as racial, ethnic, sexual, and gender minorities. We developed a new peer change agent (PCA) HIV prevention intervention with 3 arms: (1) an arm using an artificial intelligence (AI) planning algorithm to select PCAs; (2) a popularity arm, the standard PCA approach, operationalized as highest degree centrality (DC); and (3) an observation-only comparison group. Setting: A total of 713 YEH were recruited from 3 drop-in centers in Los Angeles, CA. Methods: Youth consented and completed a baseline survey that collected self-reported data on HIV knowledge, condom use, and social network information. A quasi-experimental pretest/posttest design was used; 472 youth (66.5% retention at 1 month postbaseline) and 415 youth (58.5% retention at 3 months postbaseline) completed follow-up. In each intervention arm (AI and DC), 20% of youth was selected as PCAs and attended a 4-hour initial training, followed by 7 weeks of half-hour follow-up sessions. Youth disseminated messages promoting HIV knowledge and condom use. Results: Using generalized estimating equation models, there was a significant reduction over time (P < 0.001) and a significant time by AI arm interaction (P < 0.001) for condomless anal sex act. There was a significant increase in HIV knowledge over time among PCAs in DC and AI arms. Conclusions: PCA models that promote HIV knowledge and condom use are efficacious for YEH. Youth are able to serve as a bridge between interventionists and their community. Interventionists should consider working with computer scientists to solve implementation problems.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/qai.0000000000002807

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Figures

FIGURE 1. Participant recruitment and flow diagram.
FIGURE 1. Participant recruitment and flow diagram.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Research works printed but not linked: 24

  1. Morton, 2018, Prevalence and correlates of youth homelessness in the United States, J Adolesc Health, 62, 14, 10.1016/j.jadohealth.2017.10.006 · DOI
  2. Caccamo, 2017, Narrative review: sexually transmitted diseases and homeless youth-what do we know about sexually transmitted disease prevalence and risk?, Sex Transm Dis, 44, 466, 10.1097/OLQ.0000000000000633 · DOI
  3. McCann, 2019, Homelessness among youth who identify as LGBTQ+: a systematic review, J Clin Nurs, 28, 2061, 10.1111/jocn.14818 · DOI
  4. Kelly, 1997, Randomised, controlled, community-level HIV-prevention intervention for sexual-risk behaviour among homosexual men in US cities. Community HIV Prevention Research Collaborative, Lancet, 350, 1500, 10.1016/S0140-6736(97)07439-4 · DOI
  5. Latkin, 2003, HIV prevention among drug users: outcome of a network-oriented peer outreach intervention, Health Psychol, 22, 332, 10.1037/0278-6133.22.4.332 · DOI
  6. Schneider, 2021, A pragmatic randomized controlled trial to increase PrEP uptake for HIV prevention: 55-week results from PrEPChicago, J Acquir Immune Defic Syndr, 86, 31, 10.1097/QAI.0000000000002518 · DOI
  7. Green, 2013, Shared risk: who engages in substance use with American homeless youth?, Addiction, 108, 1618, 10.1111/add.12177 · DOI
  8. Rice, 2010, Internet use, social networking, and HIV/AIDS risk for homeless adolescents, J Adolesc Health, 47, 610, 10.1016/j.jadohealth.2010.04.016 · DOI
  9. Rice, 2012, Position-specific HIV risk in a large network of homeless youths, Am J Public Health, 102, 141, 10.2105/AJPH.2011.300295 · DOI
  10. Valente, 2013, Gender differences in sexual risk and sexually transmitted infections correlate with gender differences in social networks among San Francisco homeless youth, J Adolesc Health, 53, 486, 10.1016/j.jadohealth.2013.05.016 · DOI
  11. Arnold, 2009, Comparisons of prevention programs for homeless youth, Prev Sci, 10, 76, 10.1007/s11121-008-0119-4 · DOI
  12. Medley, 2009, Effectiveness of peer education interventions for HIV prevention in developing countries: a systematic review and meta-analysis, AIDS Educ Prev, 21, 181, 10.1521/aeap.2009.21.3.181 · DOI
  13. 2010, Results of the NIMH collaborative HIV/sexually transmitted disease prevention trial of a community popular opinion leader intervention, J Acquir Immune Defic Syndr, 54, 204, 10.1097/QAI.0b013e3181d61def · DOI
  14. Kelly, 2004, Popular opinion leaders and HIV prevention peer education: resolving discrepant findings, and implications for the development of effective community programmes, AIDS Care, 16, 139, 10.1080/09540120410001640986 · DOI
  15. Schneider, 2015, A new HIV prevention network approach: sociometric peer change agent selection, Soc Sci Med, 125, 192, 10.1016/j.socscimed.2013.12.034 · DOI
  16. Valente, 2007, Identifying opinion leaders to promote behavior change, Health Educ Behav, 34, 881, 10.1177/1090198106297855 · DOI · Paper page
  17. Rice, 2018, Piloting the use of artificial intelligence to enhance HIV prevention interventions for youth experiencing homelessness, J Soc Soc Work Res, 9, 551, 10.1086/701439 · DOI
  18. Ahn, 2016, Utility of machine-learning approaches to identify behavioral markers for substance use disorders: impulsivity dimensions as predictors of current cocaine dependence, Front Psychiatry, 7, 34, 10.3389/fpsyt.2016.00034 · DOI
  19. Verghese, 2018, What this computer needs Is a physician: humanism and artificial intelligence, JAMA, 319, 19, 10.1001/jama.2017.19198 · DOI · Paper page
  20. Milburn, 2009, Adolescents exiting homelessness over two years: the risk amplification and abatement model, J Res Adolesc, 19, 762, 10.1111/j.1532-7795.2009.00610.x · DOI
  21. Freeman, 1994, Interpersonal proximity in social and cognitive space, Soc Cognit, 12, 223, 10.1521/soco.1994.12.3.223 · DOI
  22. Milburn, 2012, Family intervention to reduce sexual risk behavior, substance use, and delinquency among newly homeless youth, J Adolesc Health, 50, 358, 10.1016/j.jadohealth.2011.08.009 · DOI
  23. Rotheram-Borus, 2003, Reductions in HIV risk among runaway youth, Prev Sci, 4, 173, 10.1023/A:1024697706033 · DOI
  24. Bender, 2018, Enhancing risk detection among homeless youth: a randomized clinical rrial of a promising pilot intervention, J Interpers Violence, 33, 2945, 10.1177/0886260516633208 · DOI

Cited by

Sorted by citations

Metadata

Authors
Rice, E., Wilder, B., Onasch-Vera, L., DiGuiseppi, G., Petering, R., Hill, C., Yadav, A., Lee, S.-J., & Tambe, M.
Affiliations
  • Suzanne Dworak-Peck School of Social Work and Center for Artificial Intelligence in Society, University of Southern California Los Angeles, CA;
  • Center for Research on Computation and Society, John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA;
  • Lens Co;
  • College of Information Sciences and Technology, Pennsylvania State University College Station, TX; and
  • Department of Psychiatry and Biobehavioral Sciences, Division of Population Behavioral Health, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA.
Venue
JAIDS Journal of Acquired Immune Deficiency Syndromes
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2021-12-15
Volume
88
Issue
S1
Pages
S20-S26
ISSN
1525-4135
DOI
10.1097/qai.0000000000002807
PMC
PMC8579989
Cited by
20
References
1
Language
en
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (unspecified)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review