School of Public Health, Rutgers University, Piscataway, NJ, USA.
University of California, San Francisco, CA, USA.
J Urban Health. 2019 Feb;96(1):55-62. doi: 10.1007/s11524-018-0316-9.
We sought to leverage the strengths of time location sampling (TLS) and respondent-driven sampling (RDS) for surveys of hidden populations by combing elements of both methods in a new approach we call "starfish sampling." Starfish sampling entails random selection of venue-day-time units from a mapping of the locations where the population can be found, combined with short chains of peer referrals from their social networks at the venue or presenting to the study site later. Using the population of transmen in San Francisco as a case example, we recruited 122 eligible participants using starfish sampling: 79 at randomly selected venues, 11 on dating applications, and 32 by referral. Starfish sampling produced one of the largest community-recruited samples specifically for transmen to date. Starfish sampling is a flexibility method to recruit and sample hidden populations for whom conventional TLS and RDS may not work in theory or practice.
我们试图通过在一种新的方法中结合这两种方法的元素，利用时间地点抽样 (TLS) 和应答者驱动抽样 (RDS) 的优势来调查隐蔽人群，我们称之为“海星抽样”。海星抽样从人群可能存在的地点的映射中随机选择场地-日期-时间单位，同时结合参与者在现场或稍后在研究地点的社交网络中的短链同伴推荐。我们使用旧金山的跨性别男性人群作为案例示例，使用海星抽样招募了 122 名符合条件的参与者：79 名在随机选择的地点，11 名在约会应用程序上，32 名通过推荐。海星抽样产生了迄今为止针对跨性别男性的最大的社区招募样本之一。海星抽样是一种灵活的方法，用于招募和抽样隐蔽人群，对于这些人群，传统的 TLS 和 RDS 在理论上或实践中可能无法发挥作用。
