Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5510 Nathan Shock Drive, Baltimore, MD 21224-6823, United States.
Center for Behavioral Health and Justice, Wayne State University School of Social Work, 5201 Cass Ave, Detroit, Michigan, 48202, United States.
Int J Drug Policy. 2020 Jan;75:102587. doi: 10.1016/j.drugpo.2019.10.013. Epub 2019 Nov 18.
Online crowdsourcing methods have proved useful for studies of diverse designs in the behavioral and addiction sciences. The remote and online setting of crowdsourcing research may provide easier access to unique participant populations and improved comfort for these participants in sharing sensitive health or behavioral information. To date, few studies have evaluated the use of qualitative research methods on crowdsourcing platforms and even fewer have evaluated the quality of data gathered. The purpose of the present analysis was to document the feasibility and validity of using crowdsourcing techniques for collecting qualitative data among people who use drugs.
Participants (N = 60) with a history of non-medical prescription opioid use with transition to heroin or fentanyl use were recruited using Amazon Mechanical Turk (mTurk). A battery of qualitative questions was included indexing beliefs and behaviors surrounding opioid use, transition pathways to heroin and/or fentanyl use, and drug-related contacts with structural institutions (e.g., health care, criminal justice).
Qualitative data recruitment was feasible as evidenced by the rapid sampling of a relatively large number of participants from diverse geographic regions. Computerized text analysis indicated high ratings of authenticity for the provided narratives. These authenticity percentiles were higher than the average of general normative writing samples as well as than those collected in experimental settings.
These findings support the feasibility and quality of qualitative data collected in online settings, broadly, and crowdsourced settings, specifically. Future work among people who use drugs may leverage crowdsourcing methods and the access to hard-to-sample populations to complement existing studies in the human laboratory and clinic as well as those using other digital technology methods.
在线众包方法已被证明可用于行为和成瘾科学领域的各种设计研究。众包研究的远程和在线环境可能为独特的参与者群体提供更容易的访问途径，并提高这些参与者分享敏感健康或行为信息的舒适度。迄今为止，很少有研究评估定性研究方法在众包平台上的使用情况，更不用说评估所收集数据的质量了。本分析的目的是记录使用众包技术在使用毒品的人群中收集定性数据的可行性和有效性。
使用亚马逊 Mechanical Turk (mTurk) 招募了有非医疗处方类阿片类药物使用史且已转为使用海洛因或芬太尼的参与者（N=60）。包括一整套定性问题，索引了围绕阿片类药物使用、转向海洛因和/或芬太尼使用的信念和行为、以及与结构性机构（如医疗保健、刑事司法）的药物相关接触。
定性数据的招募是可行的，这表现在从不同地理区域快速招募了相对大量的参与者。计算机化的文本分析表明，提供的叙述具有较高的真实性评分。这些真实性百分比高于一般规范写作样本的平均值，也高于在实验环境中收集的平均值。
这些发现支持在线环境、广泛的定性数据收集的可行性和质量，特别是众包环境。未来在使用毒品的人群中，可以利用众包方法和对难以抽样人群的访问，补充现有的人类实验室和临床研究以及使用其他数字技术方法的研究。
