Morningness-Eveningness and Problematic Online Activities
Bibliographic record
Abstract
Abstract Online activities and problematic online behaviors have recently emerged as important research topics. However, only a few studies have explored the possible associations between these behaviors and morningness-eveningness. The authors examined whether eveningness predicts these distinct problematic online behaviors differently and directly or via mediators. The associations between eveningness and three different problematic online behaviors (problematic Internet use, problematic online gaming, and problematic social media use) were explored among a large sample of Hungarian young adults ( N = 1729, 57.2% female, M age = 22.01, SD age = 1.97) by using a self-report survey. Depression and the time spent engaging in online activities were assessed as possible mediators. The effects of age and sex were controlled for. Using structural equation modeling, the results supported the association between eveningness and the higher risk for all three problematic online behaviors and highlighted that these associations were mediated by depressive mood and time spent on the activities. In addition, eveningness also predicted PIU directly. Eveningness is a risk factor for problematic online behaviors not only because of the higher amount of time spent on the activities but also because of the worse mood associated with eveningness. The results highlight that it is important to examine the different types of online activity separately and explore the role of diverse risk factors, among them morningness-eveningness.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".