ArticleJournal of medical Internet research2015
Assessing Pictograph Recognition: A Comparison of Crowdsourcing and Traditional Survey Approaches.
Article in Journal of medical Internet research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Mapping of Crowdsourcing in Health: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Evaluation of pharmaceutical pictograms by older "turkers": A cross-sectional crowdsourced study.Research in social & administrative pharmacy : RSAP · 2021Article
- Validity of Online Screening for Autism: Crowdsourcing Study Comparing Paid and Unpaid Diagnostic Tasks.Journal of medical Internet research · 2019Article
- Crowdsourcing for Food Purchase Receipt Annotation via Amazon Mechanical Turk: A Feasibility Study.Journal of medical Internet research · 2019Article
- Comparing Amazon's Mechanical Turk Platform to Conventional Data Collection Methods in the Health and Medical Research Literature.Journal of general internal medicine · 2018Review
- The Patient Perspective on the Impact of Tenosynovial Giant Cell Tumors on Daily Living: Crowdsourcing Study on Physical Function and Quality of Life.Interactive journal of medical research · 2018Article
- Cloud Based Surveys to Assess Patient Perceptions of Health Care: 1000 Respondents in 3 days for US $300.JMIR research protocols · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCompared to traditional methods of participant recruitment, online crowdsourcing platforms provide a fast and low-cost alternative. Amazon Mechanical Turk (MTurk) is a large and well-known crowdsourcing service. It has developed into the leading platform for crowdsourcing recruitment.
objectiveTo explore the application of online crowdsourcing for health informatics research, specifically the testing of medical pictographs.
methodsA set of pictographs created for cardiovascular hospital discharge instructions was tested for recognition. This set of illustrations (n=486) was first tested through an in-person survey in a hospital setting (n=150) and then using online MTurk participants (n=150). We analyzed these survey results to determine their comparability.
resultsBoth the demographics and the pictograph recognition rates of online participants were different from those of the in-person participants. In the multivariable linear regression model comparing the 2 groups, the MTurk group scored significantly higher than the hospital sample after adjusting for potential demographic characteristics (adjusted mean difference 0.18, 95% CI 0.08-0.28, P<.001). The adjusted mean ratings were 2.95 (95% CI 2.89-3.02) for the in-person hospital sample and 3.14 (95% CI 3.07-3.20) for the online MTurk sample on a 4-point Likert scale (1=totally incorrect, 4=totally correct).
conclusionsThe findings suggest that crowdsourcing is a viable complement to traditional in-person surveys, but it cannot replace them.
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Registered trials
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.