Social Media Recruitment of Marginalized, Hard-to-Reach Populations: Development of Recruitment and Monitoring Guidelines
Bibliographic record
Abstract
BACKGROUND: Social media can be a useful strategy for recruiting hard-to-reach, stigmatized populations into research studies; however, it may also introduce risks for participant and research team exposure to negative comments. Currently, there is no published formal social media recruitment and monitoring guidelines that specifically address harm reduction for social media recruitment of marginalized populations. OBJECTIVE: The purpose of this research study was to investigate the utility, successes, challenges, and positive and negative consequences of using targeted Facebook advertisements as a strategy to recruit transgender and gender nonconforming (TGNC) people into a research study. METHODS: TGNC adults living in the Southeast Unites States were recruited via targeted Facebook advertisements over two cycles in April and June 2017. During cycle 1, researchers only used inclusion terms to recruit the target population. During cycle 2, the social media recruitment and monitoring protocol and inclusion and exclusion terms were used. RESULTS: The cycle 1 advertisement reached 8518 people and had 188 reactions, comments, and shares but produced cyberbullying, including discriminatory comments from Facebook members. Cycle 2 reached fewer people (6976) and received 166 reactions, comments, and shares but produced mostly positive comments. CONCLUSIONS: Researchers must consider potential harms of using targeted Facebook advertisements to recruit hard-to-reach and stigmatized populations. To minimize harm to participants and research staff, researchers must preemptively implement detailed social media recruitment and monitoring guidelines for monitoring and responding to negative feedback on targeted Facebook advertisements.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".