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Record W4289835821 · doi:10.2196/38015

Social Media Use for Research Participant Recruitment: Integrative Literature Review

2022· review· en· W4289835821 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Medical Internet Research · 2022
Typereview
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsCINAHLPsycINFOSocial mediaScopusMEDLINEInclusion (mineral)PsychologyQualitative researchMedical educationApplied psychologyMedicineSocial psychologyWorld Wide WebSocial scienceSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Social media tools have provided health researchers with the opportunity to engage with communities and groups in a nonconventional manner to recruit participants for health research. Using social media to advertise research opportunities and recruit participants facilitates accessibility to participants from broad geographical areas and diverse populations. However, little guidance is provided by ethics review boards for researchers to effectively use this recruitment method in their research. OBJECTIVE: This study sought to explore the literature on the use of social media for participant recruitment for research studies and identify the best practices for recruiting participants using this method. METHODS: An integrative review approach was used to synthesize the literature. A total of 5 health sciences databases, namely, EMBASE (Ovid), MEDLINE (Ovid and EBSCOhost), PsycINFO (Ovid), Scopus (Elsevier), and CINAHL Plus with Full Text (EBSCOhost), were searched using predefined keywords and inclusion and exclusion criteria. The initial search was conducted in October 2020 and was updated in February 2022. Descriptive and content analyses were applied to synthesize the results, and the findings are presented in a narrative and tabular format. RESULTS: A total of 96 records were included in this review, 83 (86%) from the initial search and 13 (14%) from the updated search. The publication year ranged between 2011 and 2022, with most publications (63/96, 66%) being from the United States. Regarding recruitment strategy, 45% (43/96) of the studies exclusively used social media, whereas 51% (49/96) used social media in conjunction with other strategies. The remaining 4% (4/96) provided guidelines and recommendations for social media recruitment. Notably, 38% (36/96) of these studies involved hard-to-reach populations. The findings also revealed that the use of social media is a cost-effective and efficient strategy for recruiting research participants. Despite the expanded use across different populations, there is limited participation of older adults in social media recruitment. CONCLUSIONS: This review provides important insights into the current use of social media for health research participant recruitment. Ethics boards and research support services in academic institutions are encouraged to explicitly provide researchers with guidelines on the use of social media for health research participant recruitment. A preliminary guideline prepared based on the findings of this review is proposed to spark further development in this area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.134
metaresearch head score (Gemma)0.431
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1340.431
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0040.001
Research integrity0.0010.013
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.912
GPT teacher head0.724
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it