The Adoption of Social Media to Recruit Participants for the Cool Runnings Randomized Controlled Trial in Australia
Bibliographic record
Abstract
BACKGROUND: Using social media to recruit specific populations for research studies is gaining popularity. Given that mothers of young children are the most active on social media, and young children are the most at risk of preventable burn injuries, social media was used to recruit mothers of young children to a burn prevention intervention. OBJECTIVE: The aim of this paper was to describe the social media recruitment methods used to enroll mothers of young children to the app-based burn prevention intervention Cool Runnings. METHODS: Participants were recruited via paid Facebook and Instagram advertisements to a 2-group, parallel, single-blinded, randomized controlled trial (RCT). The advertisements were targeted at women 18 years and older, living in Queensland, Australia, with at least 1 child aged 5 to 12 months at the time of recruitment. RESULTS: Over the 30-day recruitment period from January to February 2016, Facebook and Instagram advertisements reached 65,268 people, generating 2573 link clicks, 1161 app downloads, and 498 enrolled participants to the Cool Runnings RCT. The cost per enrolled participant was Aus $13.08. Saturdays were the most effective day of the week for advertising results. The most popular time of day for enrolments was between 5 to 11 PM. This recruitment strategy campaign resulted in a broad reach of participants from regional, rural, and remote Queensland. Participants were representative of the population in regard to age and education levels. CONCLUSIONS: To our knowledge, this is the first use of social media recruitment for an injury prevention campaign. This recruitment method resulted in the rapid and cost-effective recruitment of participants with social, geographic, and economic diversity that were largely representative of the population.
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 | 0.047 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".