Should We Be Worried about Smartphone Addiction? An Examination of Canadian Adolescents’ Feelings of Social Disconnection in the Time of COVID-19
Bibliographic record
Abstract
As the COVID-19 global pandemic limited face-to-face social contact, mental health concerns increased for adolescents. Additionally, many adolescents turned to technology to communicate with their peers, which also raised concerns about adolescent smartphone addiction. However, research has yet to examine how mental health and technology engagement are related to adolescents’ feelings of social connection—an important developmental predictor of wellbeing across the lifespan. Specifically, little is known regarding the relative risk of adolescents’ mental health concerns, a known risk factor for social disconnection and isolation and smartphone addiction in contributing to feelings of social disconnection in the time of COVID-19. The present study investigated how mental health outcomes and smartphone addiction contributed to Canadian adolescents’ (n = 1753) feelings of social disconnection during COVID-19. Between October 2020 and May 2021, data were collected from five secondary schools in and around the lower mainland of British Columbia using an online-administered self-report questionnaire. Adolescents responded to questions about their smartphone addiction, internalizing problems, and an open-ended question about their feelings of connection to others. Findings from logistic regression analyses indicated that depression was a predictor of feeling socially disconnected: however, smartphone addiction was not associated with feelings of social disconnection during COVID-19. Implications of these findings can help inform the development of prevention programs targeting adolescents at risk for social disconnection in times of increased social isolation (e.g., a global pandemic). Specifically, these findings suggest that adolescents higher in depressive symptoms, and not those higher in smartphone addiction, are the ones most at risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".