Challenges and Opportunities in Recruiting Research Participants Using Facebook: Lessons Learned from an Exemplar Study
Bibliographic record
Abstract
BACKGROUND: Facebook is a prominent social medial platform frequently used for business marketing. Researchers are starting to recognize the utility of this platform for developing research awareness, information dissemination, and more recently participant recruitment. PURPOSE: This paper will provide an overview of methods used in Facebook recruitment through an exemplar study. It will highlight successes and challenges and provide insight into future opportunities for its' use. METHODS: Two methods of Facebook recruitment are outlined in this paper: the use of Facebook groups and paid advertising. A step-by-step guide highlights how researchers can implement these specific methods of Facebook recruitment. RESULTS: Facebook was successfully utilized to recruit participants in the exemplar study. Recruitment was completed over a period of 82 days with a total cost of $157.09 Canadian dollars. CONCLUSION: Facebook is a viable method of recruiting research participants. This method can be cost-effective, timely, and efficient in comparison to traditional research recruitment methods. However, one must balance the benefits and challenges of this type of recruitment.
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 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 | 0.132 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".