Using social media to recruit research participants: a literature review
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.
Bibliographic record
Abstract
BACKGROUND: It may be challenging for researchers to recruit enough participants to have a diverse and representative sample for their studies. Usual recruitment methods that were historically effective can be difficult to use because of high costs, time constraints and geographical limitations. Social media is a low-cost, time-saving alternative. AIM: To summarise the benefits and challenges of using social media for recruitment. DISCUSSION: This article provides an overview of social media. It considers the advantages of social media for recruitment, including its cost-effectiveness, accessibility, speed and potential exposure for researchers. It also discusses the challenges of using social media for recruitment, including ethical ambiguity, homogenous sampling and questionable validity of information gathered. CONCLUSION: Using social media for research saves time and reduces costs, increasing access to hard-to-reach populations and the reach of recruitment efforts. IMPLICATIONS FOR PRACTICE: Options for researchers wishing to use social media for study recruitment are outlined, as are strategies for managing some of the challenges involved in this recruitment method.
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 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 | 0.033 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 it