A Low-Cost, Social Media–Supported Intervention for Caregivers to Enhance Toddlers’ Language Learning: Mixed Methods Feasibility and Acceptability Study
Bibliographic record
Abstract
Background Widely accessible, cost-effective early language development interventions for caregivers of young children are needed to promote optimal outcomes in children in the United States. Social media short-form videos, such as those on TikTok, may be a natural fit for delivering this type of intervention. Objective This study aims to examine the feasibility and acceptability of a low-intensity, short-term social media intervention for caregivers of young toddlers. Methods In total, 25 caregivers of children aged between 12 and 18 months participated in this study. We shared 32 short-form videos via TikTok over an 8-week period to help increase caregivers’ knowledge about early childhood communication. We examined metrics to characterize participant engagement, explored measures of changes in caregivers’ knowledge, and conducted a qualitative analysis of caregiver interviews after the intervention. Results Results indicated that most caregivers were able to consistently view the videos, with approximately 75% (16/21) viewership per video (mean 15.75 likes out of 21 possible likes), and caregivers reported positive effects of the intervention on their knowledge of how to support their child’s communication. The results of the exploratory measure of change in caregiver knowledge were positive but not statistically significant (t21=–1.357; P=.09). Caregivers offered suggestions for content and enhancements to videos for future investigations. Conclusions Low-cost, short-term social media interventions could be an effective means to equip caregivers with the information they need to advance their children’s language abilities, particularly for families from lower-income backgrounds whose access to health information about their young children may be limited.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".