Evidence map›Paper›PMID 40962118›Full record

ArticleJMIR public health and surveillance2025

Social Media Recruitment in Indigenous and Native American Populations: Challenges in the AI Age.

Nadia Diamond-Smith, Alison Comfort, Anna Epperson, Alicia R Riley, Natalie Beylin, Mary Garcia, Sarah Francis, Lucía Abascal Miguel

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Nadia Diamond-SmithInstitute for Global Health Sciences, University of California, San Francisco, 550 16th St, San Francisco, CA, 94117, United States.ORCID 0000-0002-8711-3029
Alison ComfortDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, San Francisco, United States.ORCID 0000-0001-6713-5258
Anna EppersonHealth Sciences Research Institute, University of California Merced, Merced, CA, United States.ORCID 0000-0001-9197-2582
Alicia R RileySociology Department, University of California, Santa Cruz, Santa Cruz, United States.ORCID 0000-0002-3341-6892
Natalie BeylinDepartment of Psychological Sciences, University of California Merced, Merced, CA, United States.ORCID 0000-0002-1048-9319
Mary GarciaHealth Sciences Research Institute, University of California Merced, Merced, CA, United States.ORCID 0009-0002-6758-3637
Sarah FrancisUpSwell, Seattle, WA, United States.ORCID 0000-0002-5262-2392
Lucía Abascal MiguelInstitute for Global Health Sciences, University of California, San Francisco, 550 16th St, San Francisco, CA, 94117, United States.ORCID 0000-0002-7142-8704

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Using social media recruitment for public health research presents both opportunities and challenges. Despite its increased use, few studies have detailed the practical issues, challenges encountered, and alternative strategies available for social media recruitment. This paper explores strategies for recruiting Indigenous and Native American populations in California for a study on COVID-19 vaccination and social networks. We describe different recruitment approaches, challenges faced, and pros and cons of strategies used to enhance data quality and efficiency, including survey design considerations, Facebook targeting versus use of research panels, quality assurance checks, and decisions around participant incentives. Our local setting involved recruiting Native American and Mesoamerican Indigenous individuals living in California through social media platforms. We highlight key adaptations to survey design, recruitment strategies, and data cleaning processes, noting what approaches that were effective and those that were not. Despite targeted efforts and community collaboration, recruitment was limited, and fraudulent data from bots significantly compromised data quality. Standard Facebook targeting approaches were largely unsuccessful. Our findings suggest that the increasing sophistication of artificial intelligence is becoming a substantial obstacle to authentic participant recruitment through social media. We offer recommendations to improve recruitment of hard-to-reach populations and mitigate AI-related fraud risks in future research.

Indexed as

Artificial IntelligenceIndians, North AmericanPatient SelectionSocial MediaCaliforniaCOVID-19COVID-19 VaccinesHumansCOVID-19 Vaccinesdata qualityindigenous populationsNative American populationspublic health researchsocial media recruitmentsurvey design

Identifiers

PMID40962118
PMCPMC12443489

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.