Associations between digital media use and psychotic experiences in young adults of Quebec, Canada: a longitudinal study
Bibliographic record
Abstract
Purpose: Digital media use has been associated with psychotic experiences in youth from the community, but the direction of association remains unclear. We aimed to examine between- and within-person associations of digital media use and psychotic experiences in youth.Methods: The sample included 425 participants aged 18-25 years (82.5% female) from the community, followed between May 2021 and January 2022 over 3 time points – of which 263 participants (61.9%) completed at least 2. Digital media use was self-reported as time spent daily on TV and streaming platforms, social media, and video games over the past 3 months. Psychotic experiences in the past 3 months were measured with the 15-item Community Assessment of Psychic Experiences. Associations between digital media use and psychotic experiences were estimated using a random-intercept cross-lagged panel model.Results: At the between-person level, greater digital media use was associated with higher levels of psychotic experiences (r=0.34, 95% CI: 0.15, 0.53). However, within-person changes in digital media use were not significantly associated with subsequent changes in psychotic experiences, or vice-versa. Results were similar across TV/streaming, social media and video game use, and after adjusting for age, sex, education, sleep, physical activity, and cannabis use.Conclusion: Individuals with a tendency for higher levels of digital media use also had a tendency for higher levels of psychotic experiences. Understanding this association may help personalize mental health interventions for people with psychotic experiences, which may be offered digitally to promote their accessibility.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".