The Role of Mental Well-Being and Perceived Parental Supportiveness in Adolescents’ Problematic Internet Use: Moderation Analysis
Bibliographic record
Abstract
BACKGROUND: Given the growing number of adolescents exhibiting problematic internet use (PIU) and experiencing its harmful consequences, it is important to examine the factors associated with PIU. Existing research has identified perceived parental supportiveness and adolescents' subjective mental well-being as strong predictors of PIU. However, it is unknown how these factors work together in shaping adolescents' engagement in PIU. OBJECTIVE: This paper aimed to examine the role played by adolescents' perception of parental supportiveness in conjunction with their subjective mental well-being in shaping their PIU. METHODS: The study analyzed one of the Technology & Adolescent Mental Wellness (TAM) data sets that were collected from a nationally representative cross-sectional sample. Adolescents self-reported their internet use behavior, perceived parental supportiveness, and subjective mental well-being through an online research panel survey. Hierarchical linear regression analysis with an interaction term was performed. RESULTS: A total of 4592 adolescents, aged 12 to 17 years, completed the survey. Adolescents reported a mean age of 14.61 (SD 1.68) and were 46.4% (2130/4592) female and 66.9% (3370/4592) White. Findings revealed that, controlling for adolescents' demographics and social media use, higher levels of perceived parental supportiveness (β=-.285, P<.001) and higher levels of subjective mental well-being (β=-.079, P<.001) were associated with a lower likelihood of adolescent PIU. The moderation analysis showed that the negative association between perceived parental supportiveness and PIU was stronger when adolescents reported high (vs low) levels of mental well-being (β=-.191, P<.001). CONCLUSIONS: This study shows that perceived parental supportiveness was a stronger protective factor than adolescents' mental well-being against PIU. The protective power of perceived parental supportiveness against PIU was strongest when adolescents had high mental well-being. The highest risk of PIU occurred when adolescents' mental well-being was high, but parents were perceived as unsupportive. Our findings suggest that parental supportiveness should be targeted as part of PIU prevention efforts.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".