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Online panels in social science research: Expanding sampling methods beyond Mechanical Turk.在线panel 调查在社会科学研究中的运用:超越 Mechanical Turk 的抽样方法。
Behav Res Methods. 2019 Oct;51(5):2022-2038. doi: 10.3758/s13428-019-01273-7.
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Social network interventions for health behaviours and outcomes: A systematic review and meta-analysis.社交网络干预对健康行为和结果的影响:系统评价和荟萃分析。
PLoS Med. 2019 Sep 3;16(9):e1002890. doi: 10.1371/journal.pmed.1002890. eCollection 2019 Sep.
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The contexts of heavy drinking: A systematic review of the combinations of context-related factors associated with heavy drinking occasions.重度饮酒的情境:与重度饮酒事件相关的情境相关因素组合的系统综述。
PLoS One. 2019 Jul 10;14(7):e0218465. doi: 10.1371/journal.pone.0218465. eCollection 2019.
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Addict Behav. 2019 Mar;90:409-414. doi: 10.1016/j.addbeh.2018.11.041. Epub 2018 Nov 27.
7
Relationships between social network characteristics, alcohol use, and alcohol-related consequences in a large network of first-year college students: How do peer drinking norms fit in?在一个大型的大一学生网络中,社会网络特征、饮酒行为和与饮酒相关后果之间的关系:同伴饮酒规范如何适应?
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8
Why is social network drinking associated with college students' alcohol use? Focus on psychological mediators.为什么社交网络饮酒与大学生饮酒有关?关注心理中介。
Psychol Addict Behav. 2018 Jun;32(4):456-465. doi: 10.1037/adb0000374. Epub 2018 Jun 4.
9
A text message intervention to reduce 21st birthday alcohol consumption: Evaluation of a two-group randomized controlled trial.短信干预以减少 21 岁生日时的酒精消费:一项两群组随机对照试验评估。
Psychol Addict Behav. 2018 Mar;32(2):149-161. doi: 10.1037/adb0000342. Epub 2018 Jan 25.
10
Comparing Amazon's Mechanical Turk Platform to Conventional Data Collection Methods in the Health and Medical Research Literature.将亚马逊的 Mechanical Turk 平台与健康和医学研究文献中的传统数据收集方法进行比较。
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将社交网络和事件特定信息纳入个性化反馈干预中,以减少年轻人的饮酒量。

Incorporating Social Networks and Event-Specific Information in a Personalized Feedback Intervention to Reduce Drinking Among Young Adults.

机构信息

Department of Psychology, University of Houston, 126 Heyne Bldg, Houston, TX 77204-5022, USA.

出版信息

Alcohol Alcohol. 2022 May 10;57(3):378-384. doi: 10.1093/alcalc/agac005.

DOI:10.1093/alcalc/agac005
PMID:35217858
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9086775/
Abstract

AIMS

To correct risky alcohol use among young adults, current personalized feedback interventions (PFIs) use social influence information related to distal referent groups. Although current PFIs have been effective, little consideration has been given to strategies applying more concrete sources of social influence, namely the influence of specific people within one's social network during actual drinking contexts. The current study evaluated a novel PFI, providing feedback regarding the presence or absence of particular individuals within a participant's close network along with the number of drinks consumed and consequences experienced during several specific drinking occasions.

METHODS

TurkPrime users (N = 338) identified five close others with whom they sometimes drank. They also provided details about their five most recent drinking occasions and noted which of their network members were present. Participants were then randomized to receive or not receive feedback consisting of a list of drinking events, negative consequences and who was present.

RESULTS

Feedback participants intended to consume fewer drinks over the next month relative to control (b = -0.25, P = 0.001). Analyses were inconclusive as to whether this effect was stronger for those who received more feedback (b = -0.01, P = 0.857).

CONCLUSIONS

Results provide preliminary support for the efficacy of a novel social influence-based intervention, which provides personalized feedback indicating social contexts that are more and less likely to lead to negative alcohol-related experiences.

摘要

目的

为了纠正年轻人的危险饮酒行为,目前的个性化反馈干预(PFI)使用了与远端参照群体相关的社会影响信息。尽管目前的 PFI 已经有效,但很少考虑应用更具体的社会影响来源的策略,即在实际饮酒环境中来自个人社交网络中特定人的影响。本研究评估了一种新的 PFI,该方法提供了关于参与者亲密网络中特定个体存在与否的反馈,以及在几次特定饮酒事件中消耗的饮料量和经历的后果。

方法

TurkPrime 用户(N=338)确定了五个有时与他们一起喝酒的亲密伙伴。他们还详细描述了他们最近的五次饮酒经历,并记录了网络成员中谁在场。然后,参与者被随机分配接受或不接受反馈,反馈内容包括一系列饮酒事件、负面后果和在场的人。

结果

与对照组相比,反馈组参与者计划在下个月内减少饮酒量(b=-0.25,P=0.001)。分析结果表明,对于那些收到更多反馈的人,这种效果是否更强并不明确(b=-0.01,P=0.857)。

结论

结果初步支持了一种新的基于社会影响的干预措施的有效性,该措施提供了个性化的反馈,表明了更有可能和不太可能导致负面酒精相关体验的社会环境。