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.

Plos One
|January 28, 2014
PubMed
Abstract

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.