Evidence map›Paper›PMID 23843021›Full record

SynthesisJournal of general internal medicine2014

Crowdsourcing--harnessing the masses to advance health and medicine, a systematic review.

Benjamin L Ranard, Yoonhee P Ha, Zachary F Meisel, David A Asch, Shawndra S Hill, Lance B Becker, Anne K Seymour, Raina M Merchant

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of general internal medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 120 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
120citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

120 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Mapping of Crowdsourcing in Health: Systematic Review.Journal of medical Internet research · 2018
    Pooled it
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  5. Trial
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  7. Crowdsourcing HIV Test Promotion Videos: A Noninferiority Randomized Controlled Trial in China.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2016
    Trial
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  18. Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2024
    Article
  19. Article
  20. Review

60 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Benjamin L RanardPerelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Blockley Hall, Philadelphia, PA, 19104, USA.
Yoonhee P Ha
Zachary F Meisel
David A Asch
Shawndra S Hill
Lance B Becker
Anne K Seymour
Raina M Merchant

Funding

Understanding the drivers of hospital performance for in-hospital cardiac arrestK23HL109083 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI MERCHANT, RAINA · 2011 to 2015
$692k
NHLBI NIH HHS K23 HL109083PHS HHS K23 10714038
6 · The paper itself

Abstract

objectiveCrowdsourcing research allows investigators to engage thousands of people to provide either data or data analysis. However, prior work has not documented the use of crowdsourcing in health and medical research. We sought to systematically review the literature to describe the scope of crowdsourcing in health research and to create a taxonomy to characterize past uses of this methodology for health and medical research. DATA SOURCES: PubMed, Embase, and CINAHL through March 2013. STUDY ELIGIBILITY CRITERIA: Primary peer-reviewed literature that used crowdsourcing for health research. STUDY APPRAISAL AND SYNTHESIS

methodsTwo authors independently screened studies and abstracted data, including demographics of the crowd engaged and approaches to crowdsourcing.

resultsTwenty-one health-related studies utilizing crowdsourcing met eligibility criteria. Four distinct types of crowdsourcing tasks were identified: problem solving, data processing, surveillance/monitoring, and surveying. These studies collectively engaged a crowd of >136,395 people, yet few studies reported demographics of the crowd. Only one (5 %) reported age, sex, and race statistics, and seven (33 %) reported at least one of these descriptors. Most reports included data on crowdsourcing logistics such as the length of crowdsourcing (n = 18, 86 %) and time to complete crowdsourcing task (n = 15, 71 %). All articles (n = 21, 100 %) reported employing some method for validating or improving the quality of data reported from the crowd. LIMITATIONS: Gray literature not searched and only a sample of online survey articles included. CONCLUSIONS AND IMPLICATIONS OF KEY

findingsUtilizing crowdsourcing can improve the quality, cost, and speed of a research project while engaging large segments of the public and creating novel science. Standardized guidelines are needed on crowdsourcing metrics that should be collected and reported to provide clarity and comparability in methods.

Indexed as

Biomedical ResearchCrowdsourcingDemographyElectronic Data ProcessingHumansPatient SelectionPopulation SurveillanceProblem Solving

Identifiers

PMID23843021
PMCPMC3889976

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.