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Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk
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, th...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899016/ https://www.ncbi.nlm.nih.gov/pubmed/24465580 http://dx.doi.org/10.1371/journal.pone.0085508 |
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author | Carter, Rebecca R. DiFeo, Analisa Bogie, Kath Zhang, Guo-Qiang Sun, Jiayang |
author_facet | Carter, Rebecca R. DiFeo, Analisa Bogie, Kath Zhang, Guo-Qiang Sun, Jiayang |
author_sort | Carter, Rebecca R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3899016 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38990162014-01-24 Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk Carter, Rebecca R. DiFeo, Analisa Bogie, Kath Zhang, Guo-Qiang Sun, Jiayang PLoS One Research Article 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. Public Library of Science 2014-01-22 /pmc/articles/PMC3899016/ /pubmed/24465580 http://dx.doi.org/10.1371/journal.pone.0085508 Text en © 2014 Carter et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Carter, Rebecca R. DiFeo, Analisa Bogie, Kath Zhang, Guo-Qiang Sun, Jiayang Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk |
title | Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk |
title_full | Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk |
title_fullStr | Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk |
title_full_unstemmed | Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk |
title_short | Crowdsourcing Awareness: Exploration of the Ovarian Cancer Knowledge Gap through Amazon Mechanical Turk |
title_sort | crowdsourcing awareness: exploration of the ovarian cancer knowledge gap through amazon mechanical turk |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899016/ https://www.ncbi.nlm.nih.gov/pubmed/24465580 http://dx.doi.org/10.1371/journal.pone.0085508 |
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