ArticleBMJ open quality2025
It's raining bots: how easier access to internet surveys has created the perfect storm.
Article in BMJ open quality, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Battling the Bots and Defending Against Fraudulent Responses in an International Community-Engaged Web-Based Survey With People Living With Long COVID: Methodological Study.Journal of medical Internet research · 2026Article
- "Providers had no idea what to do with me": A mixed-methods analysis of detransition/retransition support, care, and information needs among sexual and gender minority individuals.International journal of transgender health · 2026Article
- Parent perspectives on services available to autistic children in Canada: A mixed methods survey study.PloS one · 2026Article
- Centering transgender and gender nonconforming people of color in the study of minority stress, transition-related factors, and mental health.Frontiers in public health · 2026Article
- Development of a practice-based skills measure for community-based organizations implementing evidence-based interventions to advance equity.Implementation science communications · 2025Article
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
Online surveys are an increasingly common way to collect data from the public, with social media and financial incentives (e.g. gift cards) commonly used to increase participation rates. Anonymity, ease of response, and the potential to reach diverse demographics have also contributed to the popularity of online surveys. Health services research benefits from the increased accessibility that online survey-based data collection provides; however, fraudulent responses are of concern. The following article describes our team's experience with a national survey of Canadian healthcare providers being overrun with fraudulent responses and approach to ensure the validity of our survey data. We provide recommendations for research teams on how best to design their surveys, work with their institutions to implement safeguards within survey platforms, and screen completed responses. We also describe fraudulent open-text responses that we believe to have been produced with the help of artificial intelligence and are sounding the alarm for other researchers to be aware of this potential threat to data integrity. Informed by the learnings shared, researchers and research institutions can be better equipped to prevent and screen fraudulent responses to continue successfully engage the public in online research.
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.