Infant Safe Sleep Practices as Portrayed on Instagram: Observational Study
Bibliographic record
Abstract
BACKGROUND: Parenting practices are highly influenced by perceived social norms. Social norms and American Academy of Pediatrics (AAP) guidelines for infant safe sleep practices are often inconsistent. Instagram has become one of the most popular social media websites among young adults (including many expectant and new parents). We hypothesized that the majority of Instagram images of infant sleep and sleep environments are inconsistent with AAP guidelines, and that the number of "likes" for each image would not correlate with adherence of the image to these guidelines. OBJECTIVE: The objective of this study was to determine the extent of adherence of Instagram images of infant sleep and sleep environments to safe infant sleep guidelines. METHODS: We searched Instagram using hashtags that were relevant to infant sleeping practices and environments. We then used an open-source web scraper to collect images and the number of "likes" for each image from 27 hashtags. Images were analyzed for adherence to AAP safe sleep guidelines. RESULTS: A total of 1563 images (1134 of sleeping infant; 429 of infant sleep environment without sleeping infant) met inclusion criteria and were analyzed. Only 117 (7.49%) of the 1563 images were consistent with AAP guidelines. The most common reasons for inconsistency with AAP guidelines were presence of bedding (1173/1563, 75.05%) and nonrecommended sleep position (479/1134, 42.24%). The number of "likes" was not correlated with adherence of the image to AAP guidelines. CONCLUSIONS: Although individuals who use Instagram and post pictures of sleeping infants or infant sleep environments may not actually use these practices regularly, the consistent portrayal of images inconsistent with AAP guidelines reinforces that these practices are normative and may influence the practice of young parents.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".