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Published on: August 2, 2024
Crowdsourcing awareness: exploration of the ovarian cancer knowledge gap through Amazon Mechanical Turk
Rebecca R Carter1, Analisa DiFeo2, Kath Bogie3
1Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America.
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.
Insights
Public knowledge of ovarian cancer is poor, especially compared to breast cancer. This lack of awareness contributes to late-stage diagnosis and low survival rates for ovarian cancer.
Area of Science:
- Gynecologic Oncology
- Public Health Research
- Health Communication
Background:
- Ovarian cancer is the leading cause of gynecologic cancer death in the US, often diagnosed late due to a lack of early symptoms and diagnostic tools.
- A significant knowledge gap exists regarding ovarian cancer and its symptoms among the public and healthcare professionals.
- Late-stage diagnosis (Stage III+) affects 63% of patients, leading to a 5-year survival rate below 30%.
Purpose of the Study:
- To assess and compare public awareness and knowledge of ovarian cancer versus breast cancer in the US population.
- To identify the extent of the knowledge gap concerning ovarian cancer symptoms and risk factors.
Main Methods:
- A survey was designed with quality control measures and statistical analyses to assess public knowledge.
- Participants were crowdsourced via Amazon Mechanical Turk, providing a cost-effective and time-efficient method to reach a proxy US population.
- Repeated measures and bias control were employed in the survey design for accurate data collection.
Main Results:
- Public knowledge of ovarian cancer was confirmed to be relatively poor among the US population.
- Comparative analysis revealed a significant disparity in public awareness when contrasted with knowledge of breast cancer.
- The study identified specific areas of low awareness regarding ovarian cancer.
Conclusions:
- There is a critical need to improve public and professional understanding of ovarian cancer.
- The survey methodology using Amazon Mechanical Turk offers a scalable and effective model for future public health research.
- Addressing the knowledge gap is crucial for improving early detection and patient outcomes for ovarian cancer.
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