Loneliness and screen time usage over a year
Bibliographic record
Abstract
INTRODUCTION: This study investigates the stability of loneliness in adolescents over a 1-year period. Also, we examine how the use of screen time media (watching television, playing video games, surfing the Internet, and texting) predicts loneliness over a year and how loneliness predicts screen time media usage. METHODS: The study uses survey data from the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behavior Study. A large (N = 20,903; 54% female) sample of Canadian students in grades 9-11 (Time 1) and grades 10-12 (Time 2) were assessed at two-time points, 1 year apart. RESULTS: Loneliness scores were found to be stable over the 1-year period, with a slight increase. Additionally, while loneliness was associated with some screen time within the same year, the effects from loneliness or screen time variables at time one predicting the other at time two were negligible. The study also provides evidence that the various screen time media did not fit a single dimension. Finally, there were sex differences in loneliness and some of the media variables. CONCLUSIONS: Loneliness appears to increase slightly over the course of a year in high school students. Results indicated that Internet use and loneliness are related; however screen time use in one year does not have a substantial impact on loneliness a year later or vice versa. Lastly, the data suggested that researchers examine screen time behaviors individually in their investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".