Associations between digital media use behaviours, screen time and positive mental health in youth: results from the 2019 Canadian Health Survey on Children and Youth
Bibliographic record
Abstract
BACKGROUND: Limited research has examined associations between a range of digital media use (DMU) behaviours and screen time measures with positive mental health (PMH) outcomes among Canadian adolescents. This study examined these associations among a large sample of Canadian youth. METHODS: We used self-reported data from youth aged 12-17 years in the 2019 Canadian Health Survey on Children and Youth (N = 10,695). DMU behaviours included frequency of using social media, video/instant messaging and online gaming. Screen time included estimated hours spent watching content, playing video games and overall sedentary electronic device usage in the past week. PMH outcomes included self-rated mental health (SRMH), life satisfaction, happiness, autonomy, competence and relatedness. We conducted gender-stratified adjusted logistic regression analyses. RESULTS: Girls reporting using social media constantly (vs. never or less than weekly) were less likely to report high SRMH, life satisfaction, happiness, autonomy and competence, while their video/instant messaging frequency was unrelated to PMH outcomes. Social media use and video/instant messaging frequency tended to be unrelated to PMH outcomes among boys (or positively associated at moderate levels in a few exceptions). Boys and girls reporting online gaming constantly (vs. never or less than weekly) were less likely to report high happiness, autonomy, competence and relatedness. Boys watching content for 14+ (vs. < 3) hours in the past week had lower odds of high SRMH, life satisfaction and happiness, while girls watching 7+ (vs. < 3) hours had lower odds of all PMH outcomes. Boys and girls reporting 21+ (vs. < 3) hours of overall sedentary electronic device use in the past week had lower odds of all PMH outcomes. CONCLUSIONS: This study provides support for a link between some DMU behaviours and screen time measures with lower PMH among Canadian youth. Findings can assist in the promotion of public health strategies targeted towards promoting reduced and/or non-problematic DMU and screen time, and improved well-being, as well as the continued surveillance of DMU, screen time and PMH outcomes among Canadian youth.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".