Problem Technology Use, Academic Performance, and School Connectedness among Adolescents
Bibliographic record
Abstract
BACKGROUND: Little is known about the association between problem technology use in adolescents and school-related outcomes. The purpose of this study was to determine the prevalence of problem technology use and examine its association with academic performance and school connectedness in a sample of students across Ontario, Canada. METHODS: Self-reported data from a sample of 4837 students in grades 9 to 12 (mean age: 15.9 years; 49.5% females) were cross-sectionally analyzed. Ordered logistic regression models were adjusted for important covariates. RESULTS: We found that 35.8% of students used their screen device for at least 5 h a day and about 18.6% had moderate-to-serious symptoms of problem technology use, a prevalence that was higher in females (22.4%) than males (14.9%). Heavy technology use was differentially associated with lower academic performance and lower levels of school connectedness in males and females. Having moderate-to-serious symptoms of problem technology use was associated with lower academic performance among males (AOR = 0.68, 95% CI = 0.53-0.87) and females (AOR = 0.66, 95% CI = 0.52-0.84). It was also associated with less school connectedness in both males (AOR = 0.65, 95% CI = 0.50-0.86) and females (AOR = 0.63, 95% CI = 0.51-0.78). CONCLUSION: Excessive use and problem technology use are highly prevalent among secondary school students, and they are associated with lower academic performance and lower levels of school connectedness.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".