Evidence map›Paper›PMID 42763771›Full record

ArticleJournal of mixed methods research2026

Detecting and Mitigating Fraudulent Participation: Lessons Learned from a Mixed Methods Study.

Michelle Lamont, Ligyana Korki de Candido, Delane Linkiewich, Sina Negarandeh, Bruce Dick, Jennifer N Stinson, Abbie Jordan, Verena Kantere, Rachel Kelly, Paula Forgeron

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of mixed methods research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Michelle LamontSchool of Nursing, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.ORCID https://orcid.org/0000-0003-0023-2971
Ligyana Korki de CandidoSchool of Nursing, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.ORCID https://orcid.org/0000-0001-5089-4784
Delane LinkiewichDepartment of Psychology, University of Guelph, Guelph, ON, Canada.ORCID https://orcid.org/0000-0002-7678-699X
Sina NegarandehSchool of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.ORCID https://orcid.org/0009-0009-7508-8629
Bruce DickDepartments of Anesthesiology and Pain Medicine, University of Alberta, Edmonton, AB, Canada.ORCID https://orcid.org/0000-0003-0404-4927
Jennifer N StinsonChild Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, USA.
Abbie JordanDepartment of Psychology and Centre for Pain Research, University of Bath, Claverton Down, Bath, UK.ORCID https://orcid.org/0000-0003-1595-5574
Verena KantereSchool of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, Ottawa, ON, Canada.
Rachel KellyChild Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, USA.
Paula ForgeronSchool of Nursing, Faculty of Health Sciences, University of Ottawa, Ottawa, ON, Canada.ORCID https://orcid.org/0000-0002-4686-9698

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A surge in social media research recruitment has led to increased fraudulent participation, impacting studies such as our sequential mixed methods research (MMR) study on peer loneliness among adolescents with chronic pain during COVID-19. The purpose of this paper is to describe the challenges and subsequent strategies implemented to prevent and identify fraudulent participants during a MMR study that used online data collection methods. The results of the various mitigation strategies implemented are provided along with recommendations for future research. This article makes a valuable contribution to MMR literature by highlighting the threat to data integrity and detailing various mitigation strategies for different phases of MMR. These strategies should be proactively implemented by researchers to increase data integrity.

Indexed as

botsdata integrityfraudfraudulent participantsmitigation strategiesmixed methods researchonline research

Identifiers

PMID42763771
PMCPMC13589625

What OpenQuestion holds

Textmetadata
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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.