Problem Electronic Device Use in a Representative Sample of Adults in Ontario
Bibliographic record
Abstract
There is growing evidence of correlations between the amount of time spent using electronic devices in leisure and negative health outcomes. However, studies often lack indicators of problematic technology use, rarely investigate relationships between such problem use patterns and indicators of poor mental health, and use samples that are unrepresentative of the adult population. Using a representative telephone survey of adults, we applied the Problem Electronic Device Use (PEDU) scale to estimate the level of PEDU in Ontario, Canada, and the associations between PEDU scores and a wide range of outcomes with a series of multivariate logistic regressions. We found an average weekly electronic device use of 15.57 hr, excluding work or school, and moderate to severe problem technology use among 7.9% of the adult population. Moderate to severe PEDU was significantly associated with suicidal ideation, serious psychological distress, problem alcohol use, and treatment for anxiety. Implications for the findings are discussed.RésuméIl existe des preuves croissantes de corrélations entre la durée d’utilisation d’un appareil électronique à des fins de divertissement et les conséquences négatives sur la santé. On note souvent l’absence d’indicateurs de l’usage problématique des technologies dans les études qui examinent les corrélations entre cet usage et les indicateurs de troubles de santé mentale ou qui utilisent des échantillons représentatifs de la population adulte. À partir des résultats d’un sondage téléphonique représentatif mené auprès d’adultes, la présente étude a eu recours à l’échelle d’évaluation de l’usage problématique des appareils électroniques (Problem Electronic Device Use ou PEDU) pour estimer l’ampleur de ce phénomène dans la province (Ontario) et a appliqué une série de régressions logistiques multivariées pour établir des liens entre les résultats PEDU et un large éventail de conséquences sur la santé mentale. L’étude a révélé que l’usage hebdomadaire moyen des appareils électroniques chez les adultes sondés était de 15,57 heures et que 7,9 % d’entre eux rapportaient un niveau modéré à sévère d’usage problématique de la technologie. Des liens significatifs ont été établis entre l’usage problématique des appareils électroniques et plusieurs indicateurs de troubles de santé mentale et de consommation de substances psychoactives. Les implications de ces constats sont discutées ici.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".