Associations between problem technology use, life stress, and self-esteem among high school students
Bibliographic record
Abstract
BACKGROUND: Adolescence is a critical period for development, with many risk factors resulting in long-term health consequences, particularly regarding mental health. The purpose of this study was to examine the associations between problem technology use, life stress, and self-esteem in a representative sample of adolescents residing in Ontario, Canada. METHODS: Self-reported data were obtained from a cross-sectional sample of 4,748 students (57.9% females) in grades 9 to 12 (mean age: 15.9 ± 1.3 years) who participated in the 2019 Ontario Student Drug Use and Health Survey. Problem technology use was measured using the 6-item Short Problem Internet Use Test, life stress was assessed using an item from the British Columbia Adolescent Health Survey and self-esteem was assessed using a global measure from the Rosenberg Self-Esteem Scale. Ordinal logistic regression models were adjusted for age, sex, ethnoracial background, subjective socioeconomic status, body mass index z-score, tobacco cigarette smoking, alcohol consumption and cannabis use. RESULTS: We found that 18.3% of participants reported symptoms of moderate-to-high problem technology use, although symptoms were more common in females than males (22% vs. 14.7%, respectively). Moderate-to-high problem technology use was associated with 2.04 (95% CI: 1.77-2.35) times higher odds of reporting high life stress and 2.08 (95% CI: 1.76-2.45) times higher odds of reporting low self-esteem compared to all other response options. CONCLUSIONS: Findings from this study show that problem technology use is strongly associated with higher life stress and lower self-esteem in adolescents. This study supports the importance of developing and implementing effective strategies that help to mitigate the adverse effects of problem technology use on adolescent mental health.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".