“Infantgram?” recruitment of infants to a clinical sleep study via social media
Bibliographic record
Abstract
Abstract Study Objectives This study aimed to outline the strategy and outcomes of a study team in recruiting participants for an infant sleep study via social media during the COVID-19 pandemic, to assess the feasibility of recruitment via social media, and to quantitatively and qualitatively explore parental satisfaction and perceptions of recruitment via social media. Methods The assessing sleep in infants with early-onset atopic dermatitis by longitudinal evaluation (SPINDLE) study recruited infants with and without atopic dermatitis for a longitudinal study assessing sleep. Infants were recruited via social media and their parents were interviewed to explore their experience of recruitment via social media. Results In total, 57 controls and 33 cases were recruited. Of the 45 controls recruited via social media, 43 (95.6%) were recruited via Instagram and 2 (4.4%) were recruited via Twitter. Of the seven cases recruited via social media, 6 (85.7%) were recruited via Facebook (via sharing of Instagram posts by third parties on Facebook) and 1 (14.3%) was recruited via Instagram. All (100%, n = 28) mothers recruited via social media who completed the full study were satisfied with this approach to recruitment. Specific reasons why mothers reported engaging following exposure to the social media posts included the benefit of additional health checks for their baby, the benefit to scientific advancement, and the opportunity for a stimulating outing following the COVID-19 lockdowns. Conclusions Our experience highlights parents’ acceptance of recruitment via social media, the optimization of time and financial resources, and the benefit of using internet-based recruitment during a pandemic.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".