Perceived Parent-Adolescent Relationship, Perceived Parental Online Behaviors and Pathological Internet Use among Adolescents: Gender-Specific Differences
Bibliographic record
Abstract
This study examined the associations between adolescents' perceived relationships with their parents, perceived parental online behaviors, and Pathological Internet Use (PIU) among adolescents. Additional testing was carried out to determine the effect of different genders (parent and adolescent). Cross-sectional data was collected from 4,559 students aged 12 to 21 years in the cities of Beijing and Jinan, People's Republic of China. Participants responded to an anonymous questionnaire concerning their Internet use behavior, perceived parental Internet use behaviors, and perceived parent-adolescent relationship. Hierarchical linear regressions controlling for adolescents' age were conducted. Results showed different effects of parent and adolescent gender on perceived parent-adolescent relationship and parent Internet use behavior, as well as some other gender-specific associations. Perceived father-adolescent relationship was the most protective factor against adolescent PIU with perceived maternal Internet use positively predicting PIU for both male and female adolescents. However, perceived paternal Internet use behaviors positively predicted only female adolescent PIU. Results indicated a different effect pathway for fathers and mothers on boys and girls, leading to discussion of the implications for prevention and intervention.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".