Using Twitter to recruit participants for health research: An example from a caregiving study
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
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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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 | 0.028 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it