Using Social Media to Recruit Participants in Health Care Research: Case Study
Bibliographic record
Abstract
This paper presents a case study describing the use of social media, specifically Facebook and Instagram, as a valuable tool for recruiting participants in community-engaged health care studies. Drawing on the experiences of our team during a qualitative study aiming to understand the needs of Indigenous fathers and Two-Spirit parents as they transition to parenthood, we offer an in-depth exploration of our social media recruitment strategy. This strategy encompasses deliberate content creation and online engagement with local Indigenous community organizations and people. Through the implementation of this recruitment strategy, we successfully recruited 18 Indigenous fathers and 4 Two-Spirit parents to our community-engaged project. We learned that social media can be used to enhance recruitment by building community trust, engagement, tailored content for specific audiences, and adaptive strategies guided by data metrics provided by social media platforms. Our journey included several challenges, such as dealing with fraudulent participants, navigating budget and resource constraints, and facing recruitment limitations, which we also describe in detail. Our paper provides essential insights for researchers considering the use of social media as a recruitment tool but we are unsure of how to begin. Health care researchers may find our experience and recommendations helpful for developing and implementing their own effective social media recruitment strategy. Meanwhile, sharing our experience contributes to the broader understanding of the role of social media in participant recruitment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".