Observational studyJournal of medical Internet research2020
You Get What You Pay for on Health Care Question and Answer Platforms: Nonparticipant Observational Study.
Observational study in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Quality Measurement of Consumer Health Questions: Content and Language Perspectives.Journal of medical Internet research · 2024Article
- Factors Influencing the Purchase Intention for Online Health Popular Science Information Based on the Health Belief Model.Behavioral sciences (Basel, Switzerland) · 2023Article
- Factors Affecting Respondents' Strategies in Answering Queries Related to the Field of Health on Social Question Answering (SQA) Websites.Medical journal of the Islamic Republic of Iran · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeeking health information on the internet is very popular despite the debatable ability of lay users to evaluate the quality of health information and uneven quality of information available on the Web. Consulting the internet for health information is pervasive, particularly when other sources are inaccessible because of time, distance, and money constraints or when sensitive or embarrassing questions are to be explored. Question and answer (Q&A) platforms are Web-based services that provide personalized health advice upon the information seekers' request. However, it is not clear how the quality of health advices is ensured on these platforms.
objectiveThe objective of this study was to identify how platform design impacts the quality of Web-based health advices and equal access to health information on the internet.
methodsA total of 900 Q&As were collected from 9 Q&A platforms with different design features. Data on the design features for each platform were generated. Paid physicians evaluated the data to quantify the quality of health advices. Guided by the literature, the design features that affected information quality were identified and recorded for each Q&A platform. The least absolute shrinkage and selection operator and unbiased regression tree methods were used for the analysis.
resultsQ&A platform design and health advice quality were related. Expertise of information providers (beta=.48; P=.001), financial incentive (beta=.4; P=.001), external reputation (beta=.28; P=.002), and question quality (beta=.12; P=.001) best predicted health advice quality. Virtual incentive, Web 2.0 mechanisms, and reputation systems were not associated with health advice quality.
conclusionsAccess to high-quality health advices on the internet is unequal and skewed toward high-income and high-literacy groups. However, there are possibilities to generate high-quality health advices for free.
Indexed as
Identifiers
What OpenQuestion holds
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.