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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".