Using Twitter to recruit participants for health research: An example from a caregiving study
Bibliographic record
Abstract
Twitter has the potential to optimize research conduct, but more research is needed around the nature of study-related tweets and strategies for optimizing reach. In the context of our caregiving study, we aimed to describe the nature and extent of study-related tweets, the extent to which they were shared by others, and their potential reach. To do so, we conducted a secondary analysis of our Twitter recruitment. We aggregated and categorized study-related tweets and analyzed the reach of the 10 most retweeted tweets. Results indicated that of 71 caregivers, 27 were recruited via Twitter. General recruitment tweets were most-shared by users. Tweet reach ranged from 5273 to 62,144 users. Twitter caregivers were demographically comparable to non-Twitter caregivers but had higher Internet proficiency and fewer children. Overall, using a personal Twitter account can expand the reach of study recruitment. Future research should compare different recruitment strategies and explore characteristics that may challenge the heterogeneity of Twitter samples.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".