Problem Technology Use, Academic Performance, and School Connectedness among Adolescents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it