Social Media Recruitment as a Potential Trigger for Vulnerability: Multistakeholder Interview Study
Bibliographic record
Abstract
Background: More clinical studies use social media to increase recruitment accrual. However, empirical analyses focusing on the ethical aspects pertinent when targeting patients with vulnerable characteristics are lacking. Objective: This study aims to explore expert and patient perspectives on vulnerability in the context of social media recruitment and seeks to explore how social media can reduce or amplify vulnerabilities. Methods: As part of an international consortium that tests a therapeutic vaccine against hepatitis B (TherVacB), we conducted 30 qualitative interviews with multidisciplinary experts in social media recruitment (from the fields of clinical research, public relations, psychology, ethics, philosophy, law, and social sciences) about the ethical, legal, and social challenges of social media recruitment. We triangulated the expert assessments with the perceptions of 6 patients with hepatitis B regarding social media usage and attitudes relative to their diagnosis. Results: Experts perceived social media recruitment as beneficial for reaching hard-to-reach populations and preserving patient privacy. Features that may aggravate existing vulnerabilities are the acontextual point of contact, potential breaches of user privacy, biased algorithms disproportionately affecting disadvantaged groups, and technological barriers such as insufficient digital literacy skills and restricted access to relevant technology. We also report several practical recommendations from experts to navigate these triggering effects of social media recruitment, including transparent communication, addressing algorithm bias, privacy education, and multichannel recruitment. Conclusions: Using social media for clinical study recruitment can mitigate and aggravate potential study participants' vulnerabilities. Researchers should anticipate and address the outlined triggering effects within this study's design and proactively define strategies to overcome them. We suggest practical recommendations to achieve this.
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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 | 0.056 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".