Explaining Adherence to American Academy of Pediatrics Screen Time Recommendations With Caregiver Awareness and Parental Motivation Factors: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: With the increasing integration of technology into society, it is advisable that researchers explore the effects of repeated digital media exposure on our most vulnerable population-infants. Excessive screen time during infancy has been linked to delays in language, literacy, and self-regulation. OBJECTIVE: This study explores the awareness of and adherence to the American Academy of Pediatrics' (AAP) recommendations related to avoiding screen time for infants younger than 2 years and the motivational factors associated with screen time exposure. METHODS: A mixed methods survey design was used to gather responses from 178 mothers of infants younger than 2 years. The measures included infant screen time use and duration, maternal awareness of screen time use recommendations, and motivations related to screen time exposure. A variety of statistical procedures were used to explore associations between caregiver awareness of and adherence to AAP guidelines for screen time exposure, motivations related to screen time for infants, and the duration of infant screen time exposure. RESULTS: The results indicated that 62.2% (111/178) of mothers were aware of the AAP screen time recommendations, but only 46.1% (82/178) could cite them accurately, and most mothers learned of them via the internet or from a medical professional. Mothers who were aware of the guidelines allowed significantly less screen time for infants than those who were unaware (P=.03). In addition, parents who adhered to the AAP guidelines reported significantly less infant screen time per day than those who did not adhere (P<.001). Among mothers who reported not adhering to the guidelines, the greatest motivation for allowing screen time was perceived educational benefits. Less educated mothers rated an infant's relaxation as a motivational factor in allowing screen time significantly higher than more highly educated mothers (P=.048). The regression analysis indicated that none of the parental motivation factors predicted daily infant screen time. CONCLUSIONS: These results indicate 2 key approaches to improving adherence to screen time recommendations. First, the awareness of the AAP recommendations needs to be increased, which tends to improve adherence. Second, the myth that screen time can be educational for infants needs to be dispelled.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".