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Related Experiment Video

Updated: May 17, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

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Published on: July 27, 2018

Evaluating Crowdsourced Data Collection for Carceral Death Surveillance: Pilot Study Using Amazon Mechanical Turk.

Emily Wang1, Julia Healey-Parera1, Amy Duan1

  • 1Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, United States.

JMIR Formative Research
|May 15, 2026
PubMed
Summary
This summary is machine-generated.

Crowdsourcing using Amazon Mechanical Turk (MTurk) rapidly extracted data on deaths in custody but yielded low-quality results due to poor worker agreement. Improved task design or AI support is needed for reliable data collection on deaths in correctional facilities.

Keywords:
Amazon Mechanical Turkcarceral healthcrowdsourcingdeaths in custodydigital epidemiologyhealth equitymortality surveillancestructured data abstraction

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Area of Science:

  • Public Health
  • Criminology
  • Data Science

Background:

  • Incarcerated populations face elevated health risks, yet deaths in custody are underreported and poorly monitored.
  • Existing reporting mechanisms like the Death in Custody Reporting Act have inconsistent, delayed, and inaccessible data.
  • Researchers often rely on correctional agency press releases, which lack standardized formats for data extraction.

Purpose of the Study:

  • To evaluate the utility of Amazon Mechanical Turk (MTurk) for extracting structured information from press releases concerning deaths in custody.
  • To assess the feasibility of crowdsourcing for timely data collection on deaths within correctional facilities.

Main Methods:

  • 144 press releases on deaths in custody (2000-2023) from state prisons and ICE were assigned to 3 MTurk workers each.
  • Workers completed a 16-question form based on Death in Custody Reporting Act variables.
  • Data quality was assessed via strict and 2-way concordance, with qualitative review of errors.

Main Results:

  • The task was completed rapidly (within 48 hours) but showed low agreement among crowd workers.
  • Strict concordance rates were low: 14.2% for age, 12.3% for race/ethnicity, and 11.4% for date of birth.
  • Qualitative review revealed frequent errors, missing data, and inattentive responses, indicating insufficient data quality for complex abstraction.

Conclusions:

  • While MTurk facilitated quick data collection, the extracted information from carceral press releases was of low quality.
  • General crowdsourcing platforms require enhanced training or oversight for complex data abstraction tasks.
  • Future improvements may involve refined task design, AI integration, and ultimately, standardized reporting by correctional institutions to improve surveillance of deaths in custody.