Screen Time and Mental Health in Canadian Youth: An Examination of Nationally Representative Data
Bibliographic record
Abstract
As screens have become ubiquitous in modern-day society, investigating the effects of high screen time on mental health is highly warranted. In the past decade, many studies have determined that higher levels of screen time engagement are associated with adverse mental health outcomes like anxiety and depression. However, the nature of the relationship between screen time and mental health requires further investigation to gain a better understanding of its mechanisms and properties. The purpose of this study is to utilize a nationally representative data set to (1) examine how factors like sex, age, and socioeconomic status moderate the relationship between screen time and mental health in Canadian youth and (2) determine whether this relationship supports the Goldilocks hypothesis or an exposure-response curve. It was hypothesized that (1) young, female, lower socioeconomic status individuals will be more strongly associated with poor mental health, and that (2) mental health will peak at low screen time usage, therefore, supporting an exposure-response curve. A series of moderation analyses concluded that young, male, lower socioeconomic status individuals strongly moderated the relationship between screen time and poor mental health compared to their counterparts. Furthermore, three out of the four mental health (presence of mood disorder, presence of anxiety disorder, and depression severity) measures peaked at an average of 12 hours and 19 minutes of screen time per week, hence, supporting the exposure-response curve.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".