ArticleEvaluation & the health professions2026
Planning for New Threats to Online Research Data Validity: The Issue of Computer-Using Agents.
Article in Evaluation & the health professions, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Participants using GenAI in online studies: an overview and recommendations for researchers and reviewers.Research integrity and peer review · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing numbers of research studies are completed entirely online, often using "crowdsourcing" platforms to recruit participants, such as Amazon's Mechanical Turk (MTurk) or Prolific. Over time, crowdsourced data collection has faced multiple threats to validity including bots, inattention, and misrepresentation. Fortunately, research and development of quality control techniques has resulted in approaches to conducting credible research using crowdsourcing platforms. However, advances in large language models (LLMs), particularly computer-using agents (CUAs), such as OpenAI's "Operator," may pose novel threats to the validity of crowdsourced research data in the future. This brief report explores how and why that might be the case and encourages further discussion of this issue.
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.