Evidence map›Paper›PMID 29764795›Full record

SynthesisJournal of medical Internet research2018

Mapping of Crowdsourcing in Health: Systematic Review.

Perrine Créquit, Ghizlène Mansouri, Mehdi Benchoufi, Alexandre Vivot, Philippe Ravaud

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
–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

55 citing papers in PubMed.

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  4. Assessment of laryngologists' ratings on physician review websites.World journal of otorhinolaryngology - head and neck surgery · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Perrine CréquitINSERM UMR1153, Methods Team, Epidemiology and Statistics Sorbonne Paris Cité Research Center, Paris Descartes University, Paris, France.ORCID 0000-0001-7836-0717
Ghizlène MansouriINSERM UMR1153, Methods Team, Epidemiology and Statistics Sorbonne Paris Cité Research Center, Paris Descartes University, Paris, France.ORCID 0000-0001-5621-4890
Mehdi BenchoufiCentre d'Epidémiologie Clinique, Hôpital Hôtel Dieu, Assistance Publique des Hôpitaux de Paris, Paris, France.ORCID 0000-0003-1948-719X
Alexandre VivotINSERM UMR1153, Methods Team, Epidemiology and Statistics Sorbonne Paris Cité Research Center, Paris Descartes University, Paris, France.ORCID 0000-0001-9281-3129
Philippe RavaudINSERM UMR1153, Methods Team, Epidemiology and Statistics Sorbonne Paris Cité Research Center, Paris Descartes University, Paris, France.ORCID 0000-0001-8264-9206

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCrowdsourcing involves obtaining ideas, needed services, or content by soliciting Web-based contributions from a crowd. The 4 types of crowdsourced tasks (problem solving, data processing, surveillance or monitoring, and surveying) can be applied in the 3 categories of health (promotion, research, and care).

objectiveThis study aimed to map the different applications of crowdsourcing in health to assess the fields of health that are using crowdsourcing and the crowdsourced tasks used. We also describe the logistics of crowdsourcing and the characteristics of crowd workers.

methodsMEDLINE, EMBASE, and ClinicalTrials.gov were searched for available reports from inception to March 30, 2016, with no restriction on language or publication status.

resultsWe identified 202 relevant studies that used crowdsourcing, including 9 randomized controlled trials, of which only one had posted results at ClinicalTrials.gov. Crowdsourcing was used in health promotion (91/202, 45.0%), research (73/202, 36.1%), and care (38/202, 18.8%). The 4 most frequent areas of application were public health (67/202, 33.2%), psychiatry (32/202, 15.8%), surgery (22/202, 10.9%), and oncology (14/202, 6.9%). Half of the reports (99/202, 49.0%) referred to data processing, 34.6% (70/202) referred to surveying, 10.4% (21/202) referred to surveillance or monitoring, and 5.9% (12/202) referred to problem-solving. Labor market platforms (eg, Amazon Mechanical Turk) were used in most studies (190/202, 94%). The crowd workers' characteristics were poorly reported, and crowdsourcing logistics were missing from two-thirds of the reports. When reported, the median size of the crowd was 424 (first and third quartiles: 167-802); crowd workers' median age was 34 years (32-36). Crowd workers were mainly recruited nationally, particularly in the United States. For many studies (58.9%, 119/202), previous experience in crowdsourcing was required, and passing a qualification test or training was seldom needed (11.9% of studies; 24/202). For half of the studies, monetary incentives were mentioned, with mainly less than US $1 to perform the task. The time needed to perform the task was mostly less than 10 min (58.9% of studies; 119/202). Data quality validation was used in 54/202 studies (26.7%), mainly by attention check questions or by replicating the task with several crowd workers.

conclusionsThe use of crowdsourcing, which allows access to a large pool of participants as well as saving time in data collection, lowering costs, and speeding up innovations, is increasing in health promotion, research, and care. However, the description of crowdsourcing logistics and crowd workers' characteristics is frequently missing in study reports and needs to be precisely reported to better interpret the study findings and replicate them.

Indexed as

AdultCrowdsourcingData CollectionHumanscrowdsourcinghealthreview [publication type]

Identifiers

PMID29764795
PMCPMC5974463

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.