Parental Attitudes on Social Media Monitoring for Youth: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Online environments dominate the daily lives of American youth and pose evolving challenges to their health and well-being. Recent national poll data indicate that social media overuse, internet safety, and online bullying are among parents' top child health concerns, particularly during the COVID-19 pandemic. While parents are uniquely positioned to help youth navigate social media, their attitudes on monitoring media use may be impacted by a myriad of personal and family factors. Objective: This study aimed to examine factors associated with parental attitudes about monitoring social media use among youth. Methods: Data were analyzed from the Voices of Child Health in Chicago Parent Panel Survey, administered to parents over the web and by telephone. Parents with at least 1 child aged ≥11 years responded to questions about bullying and social media monitoring from May to July 2020. The primary outcome was their response to the following question: "Do you think parents should monitor their children's use of social media platforms such as Facebook, Twitter, and Instagram?" Bivariate analyses and multivariable logistic regression were used to examine parental agreement with frequent social media monitoring and concerns about bullying, adjusted for sociodemographic characteristics. Analyses were weighted to represent the parent population of Chicago. Results: Among 1613 survey respondents, the analyzed sample included 808 parents with at least 1 child aged ≥11 years. Overall, 62.9% (n=566) of parents agreed with frequent parental monitoring of their children's social media use. Compared with parents aged ≤35 years, parents who were >35 years old were significantly less likely to agree with frequent social media monitoring (adjusted odds ratio [aOR] 0.45, 95% CI 0.25-0.81). Parents expressing a high level of concern regarding the effects of bullying were more likely to agree with frequent monitoring of youth social media (aOR 2.15, 95% CI 1.24-3.73). Conclusions: Parents' personal characteristics and concerns about bullying may influence their attitudes toward monitoring social media use among youth. Given the potential impact of these attitudes on parental monitoring behaviors and the subsequent health impact on youth, pediatricians should consider these factors when counseling about bullying and social media. Child health professionals can support families in developing a safe media use plan that fits family circumstances.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".