Binge-Watching and Mental Health Problems: A Systematic \nReview and Meta-Analysis
Bibliographic record
Abstract
Abstract: Background: Binge-watching, the viewing of online videos or streamed content, may be \nassociated with different types of mental health problems. The present study aimed to investigate \nthe associations between binge-watching and five mental health concerns including depression, \nloneliness, sleep problems, anxiety, and stress. Methods: Academic databases of PubMed, Scopus, \nWeb of Science, ProQuest, PsycINFO, and Psych Articles were systematically searched through \nFebruary of 2022. The Newcastle–Ottawa Scale was used to assess the methodological quality. A \nmeta-analysis was performed on Fisher’s z values as effect sizes, using a random effect model. \nPublication bias, small study effect, and moderators in this association were assessed. Results: Bingewatching \nwas significantly associated with the five types of mental health concerns with the most \nrobust correlations found with stress (0.32) and anxiety (0.25). Stronger associations between bingewatching \nand two types of mental health problems (depression and sleep problems) were found \nduring the COVID-19 pandemic than before the pandemic. Moreover, stronger associations between \nbinge-watching and two types of mental health problems (stress and sleep problems) were found \nin developing countries than in developed countries. Conclusions: The associations between bingewatching \nand mental health concerns were significant and positive. Programs and interventions to \nreduce binge-watching should be considered and tested.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.022 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".