Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk

被引:45
作者
Carter, Rebecca R. [1 ]
DiFeo, Analisa [2 ]
Bogie, Kath [3 ,4 ]
Zhang, Guo-Qiang [2 ]
Sun, Jiayang [1 ]
机构
[1] Case Western Reserve Univ, Sch Med, Dept Epidemiol & Biostat, Cleveland, OH 44106 USA
[2] Case Western Reserve Univ, Case Comprehens Canc Ctr, Cleveland, OH 44106 USA
[3] Case Western Reserve Univ, Dept Orthopaed, Cleveland, OH 44106 USA
[4] Case Western Reserve Univ, Dept Biomed Engn, Cleveland, OH 44106 USA
关键词
BREAST; WOMEN; MAMMOGRAPHY; STATISTICS; ACCURACY; SYMPTOMS;
D O I
10.1371/journal.pone.0085508
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: Ovarian cancer is the most lethal gynecologic disease in the United States, with more women dying from this cancer than all gynecological cancers combined. Ovarian cancer has been termed the "silent killer" because some patients do not show clear symptoms at an early stage. Currently, there is a lack of approved and effective early diagnostic tools for ovarian cancer. There is also an apparent severe knowledge gap of ovarian cancer in general and of its indicative symptoms among both public and many health professionals. These factors have significantly contributed to the late stage diagnosis of most ovarian cancer patients (63% are diagnosed at Stage III or above), where the 5-year survival rate is less than 30%. The paucity of knowledge concerning ovarian cancer in the United States is unknown. Methods: The present investigation examined current public awareness and knowledge about ovarian cancer. The study implemented design strategies to develop an unbiased survey with quality control measures, including the modern application of multiple statistical analyses. The survey assessed a reasonable proxy of the US population by crowdsourcing participants through the online task marketplace Amazon Mechanical Turk, at a highly condensed rate of cost and time compared to traditional recruitment methods. Conclusion: Knowledge of ovarian cancer was compared to that of breast cancer using repeated measures, bias control and other quality control measures in the survey design. Analyses included multinomial logistic regression and categorical data analysis procedures such as correspondence analysis, among other statistics. We confirmed the relatively poor public knowledge of ovarian cancer among the US population. The simple, yet novel design should set an example for designing surveys to obtain quality data via Amazon Mechanical Turk with the associated analyses.
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页数:10
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