A mediation analysis of the influence of sleep on the relationship between smartphone screen time and youth mental health
Bibliographic record
Abstract
Research using subjective measures suggests that young people spend large amounts of their leisure time using digital media, which may affect their mental health. Of particular concern is that smartphone screen-time may replace health-promoting activities such as sleep and thereby contribute to mental health problems. Considering that previous studies primarily relied on subjective reports of screen-time and its effects on youth mental health, the objective of the current research was to examine whether screen-time objectively measured via mobile sensing was associated with internalizing (e.g., anxiety, depression) and externalizing (e.g., impulsivity, aggression) symptoms and whether this association was mediated by reduced sleep duration. 407 Canadian youths aged 15–25 completed questionnaires about their mental health symptoms and used a mobile sensing app to measure screen-time and sleep for at least 14 days. The association between screen-time and mental health symptoms and the mediation of sleep duration were tested by fitting structural equation models. Results suggested that objectively measured smartphone screen-time was indirectly associated with externalizing symptoms through reduced sleep duration, but showed no significant association with internalizing symptoms. These findings complement previous research that used subjective measures and highlight the need to provide support and resources to youth to promote healthy screen use and healthy sleep habits.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".