Promoting healthy digital device usage: recommendations for youth and parents
Bibliographic record
Abstract
Young people's usage of digital devices is currently a central topic of interest for researchers, clinicians and the general public, particularly with regards to the impact of social media on adolescents’ mental health. Notably, the duration of screen time is not the primary determinant of mental health outcomes1, 2. Rather, the “quality” of an individual's device usage patterns, experiences and interactions online, and how they correlate with other lifestyle variables (e.g., sedentary time and sleep) appear to matter most1, 3, 4. Other than avoiding the more clear-cut “online harms” (e.g., addictive behaviors, cyberbullying, and online blackmail or exploitation), there is a lack of consensus on how youth can improve the “quality” of their online time. This is in part because the details of what constitutes “healthy” device usage are unclear, and likely differ with regards to sociodemographic factors1. Here we sought to produce a simplified set of recommended actions to promote adolescents’ healthy digital device usage. We assembled a multidisciplinary team of individuals with expertise across child and adolescent mental health, social media research, behavior change interventions, and public health. We then identified and reviewed recently published guideline/recommendation articles, online resources and reports from independent think tanks – particularly those that included feedback from young people themselves. We checked these resources for directly actionable advice, rather than general principles on healthy usage patterns. We then considered the recommended actions from such documents alongside the underlying scientific evidence and the team's experience, in order to put forward the top three tips for healthy device usage in adolescents. We also produced a further set of recommendations for parents who wish to implement such changes in their family units. As digital device usage has been increasing worldwide, the impact on youth mental health has emerged as a central concern. We sought to produce a set of best-practice approaches, on the basis of available evidence and guidelines, for adolescents and their parents looking to improve their device usage patterns. Ultimately, however, managing this issue at a societal level will require a whole system approach, involving partnerships between governments, social media companies, and health care organizations. To propel this, more high-quality research is urgently needed to determine what actions policy makers, clinicians and the public can take, including the perspectives of young people themselves.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".