Gender Difference in Internet Use and Internet Problems among Quebec High School Students
Bibliographic record
Abstract
OBJECTIVES: There are presently no data available concerning Internet addiction (IA) problems among adolescents in Canada and the province of Quebec. The goal of this study is thus to document and compare the influence of gender on Internet use and addiction. METHOD: The study data were collected from a larger research project on gambling among adolescents. Activities conducted online (applications used and time spent) as well as answers to the Internet Addiction Test (IAT) were collected from 3938 adolescents from grades 9 to 11. The two most often employed cut-off points for the IAT in the literature were documented: (40-69 and 70+) and (50+). RESULTS: Boys spent significantly more time on the Internet than did girls. A greater proportion of the girls made intense use of social networks, whereas a greater proportion of the boys made intense use of massively multiplayer online role-playing games, online games, and adult sites. The proportion of adolescents with a potential IA problem varied according to the cut-off employed. When the cut-off was set at 70+, 1.3% of the adolescents were considered to have an IA, while 41.7% were seen to be at risk. At a 50+ cut-off, 18% of the adolescents were considered to have a problem. There was no significant difference between the genders concerning the proportion of adolescents considered to be at risk or presenting IA problems. Finally, analysis of the percentile ranks would seem to show that a cut-off of 50+ better describes the category of young people at risk. CONCLUSIONS: The results of this study make it possible to document Internet use and IA in a large number of Quebec adolescents.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".