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Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial

BACKGROUND: HIV self-testing (HIVST), especially the secondary distribution of HIVST (SD-HIVST) initiated by sexual health influencers (SHIs), has been recognized as an effective strategy in promoting HIV testing, especially among men who have sex with men (MSM). This quasi-experimental study aimed...

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Autores principales: Lu, Ying, Ni, Yuxin, Wang, Qianyun, Jing, Fengshi, Zhou, Yi, He, Xi, Huang, Shanzi, Dai, Wencan, Wu, Dan, Tucker, Joseph D., Jiang, Hongbo, Huang, Liqun, Tang, Weiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480079/
https://www.ncbi.nlm.nih.gov/pubmed/34583667
http://dx.doi.org/10.1186/s12889-021-11817-2
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author Lu, Ying
Ni, Yuxin
Wang, Qianyun
Jing, Fengshi
Zhou, Yi
He, Xi
Huang, Shanzi
Dai, Wencan
Wu, Dan
Tucker, Joseph D.
Jiang, Hongbo
Huang, Liqun
Tang, Weiming
author_facet Lu, Ying
Ni, Yuxin
Wang, Qianyun
Jing, Fengshi
Zhou, Yi
He, Xi
Huang, Shanzi
Dai, Wencan
Wu, Dan
Tucker, Joseph D.
Jiang, Hongbo
Huang, Liqun
Tang, Weiming
author_sort Lu, Ying
collection PubMed
description BACKGROUND: HIV self-testing (HIVST), especially the secondary distribution of HIVST (SD-HIVST) initiated by sexual health influencers (SHIs), has been recognized as an effective strategy in promoting HIV testing, especially among men who have sex with men (MSM). This quasi-experimental study aimed to evaluate whether SHIs identified through the ensemble machine learning approach can distribute more HIVST than those who identified by the empiricalscale. METHODS: We will recruit eligible adults (≥18 years old) who were assigned male gender at birth, and willing to participate in potential SD-HIVST online. Participants will be assigned randomly to two groups (scale group or machine learning group), followed by a separate process of SHI identification based on the group assignment. After identification, all index participants (defined as identified SHIs who are verbally consented to participate in SD-HIVST or who directly order HIVST kits) will follow the same procedure for SD-HIVST acquisition and distribution. Index participants can order HIVST online and distribute them to members within their social networks (defined as alters) in-person or virtually through a personalized peer referral link. Once a unique alter uploads a photographed test result to the platform, both the alter and the corresponding index participant will receive a fixed incentive of 3 USD. The index MSM can order up to five HIVST in the first three months and ten HIVST in the following three months. Each index participant will need to complete a baseline survey at the first-time ordering and one to two follow-upbased on the times of ordering,, three months after ordering. This trial will be comparing 1) the mean number of alters motivated by each index participant in each group and 2) the mean number of newly-tested alters motivated by each index participant in each group. DISCUSSION: In promoting the efficacy of identifying SHIs for SD-HIVST, our study has the potential to enhance testing coverage, particularly among marginalized individuals and those who are reluctant to for HIV and other sexually transmitted infections. TRIAL REGISTRATION: We registered the study on the Chinese Clinical Trial Registry website on 4th November 2021, with registration number ChiCTR2000039632.
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spelling pubmed-84800792021-09-30 Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial Lu, Ying Ni, Yuxin Wang, Qianyun Jing, Fengshi Zhou, Yi He, Xi Huang, Shanzi Dai, Wencan Wu, Dan Tucker, Joseph D. Jiang, Hongbo Huang, Liqun Tang, Weiming BMC Public Health Study Protocol BACKGROUND: HIV self-testing (HIVST), especially the secondary distribution of HIVST (SD-HIVST) initiated by sexual health influencers (SHIs), has been recognized as an effective strategy in promoting HIV testing, especially among men who have sex with men (MSM). This quasi-experimental study aimed to evaluate whether SHIs identified through the ensemble machine learning approach can distribute more HIVST than those who identified by the empiricalscale. METHODS: We will recruit eligible adults (≥18 years old) who were assigned male gender at birth, and willing to participate in potential SD-HIVST online. Participants will be assigned randomly to two groups (scale group or machine learning group), followed by a separate process of SHI identification based on the group assignment. After identification, all index participants (defined as identified SHIs who are verbally consented to participate in SD-HIVST or who directly order HIVST kits) will follow the same procedure for SD-HIVST acquisition and distribution. Index participants can order HIVST online and distribute them to members within their social networks (defined as alters) in-person or virtually through a personalized peer referral link. Once a unique alter uploads a photographed test result to the platform, both the alter and the corresponding index participant will receive a fixed incentive of 3 USD. The index MSM can order up to five HIVST in the first three months and ten HIVST in the following three months. Each index participant will need to complete a baseline survey at the first-time ordering and one to two follow-upbased on the times of ordering,, three months after ordering. This trial will be comparing 1) the mean number of alters motivated by each index participant in each group and 2) the mean number of newly-tested alters motivated by each index participant in each group. DISCUSSION: In promoting the efficacy of identifying SHIs for SD-HIVST, our study has the potential to enhance testing coverage, particularly among marginalized individuals and those who are reluctant to for HIV and other sexually transmitted infections. TRIAL REGISTRATION: We registered the study on the Chinese Clinical Trial Registry website on 4th November 2021, with registration number ChiCTR2000039632. BioMed Central 2021-09-28 /pmc/articles/PMC8480079/ /pubmed/34583667 http://dx.doi.org/10.1186/s12889-021-11817-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Study Protocol
Lu, Ying
Ni, Yuxin
Wang, Qianyun
Jing, Fengshi
Zhou, Yi
He, Xi
Huang, Shanzi
Dai, Wencan
Wu, Dan
Tucker, Joseph D.
Jiang, Hongbo
Huang, Liqun
Tang, Weiming
Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial
title Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial
title_full Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial
title_fullStr Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial
title_full_unstemmed Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial
title_short Effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of HIV self-testing among men who have sex with men in China: study protocol for a quasi-experimental trial
title_sort effectiveness of sexual health influencers identified by an ensemble machine learning model in promoting secondary distribution of hiv self-testing among men who have sex with men in china: study protocol for a quasi-experimental trial
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8480079/
https://www.ncbi.nlm.nih.gov/pubmed/34583667
http://dx.doi.org/10.1186/s12889-021-11817-2
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