Adolescent Participation and Flow in Physically Active Leisure and Electronic Media Activities: Testing the Displacement Hypothesis
Bibliographic record
Abstract
The relationships between participation in various types of electronic media and time spent on other leisure activities have not been examined to any great extent and the research findings in the reported studies have been mixed. In this article, research is described in which 219 adolescents’ electronic media use (TV/video watching, computer/video game playing and Internet/web surfing) was monitored using a time use survey and the experiential sampling method. Behavioral and experiential data were analyzed with a particular focus on the impact of electronic media use on participation in physically active leisure. The findings were consistent with the “displacement” hypothesis. Those adolescents who engaged in higher levels of electronic media activities, in particular, TV/video watching and computer/video game playing, not only reported lower levels of physically active leisure but had more free time as a result of displacing non-leisure activities such as homework. While the overall patterns were similar for females and males, TV/video viewing among females and computer/video gaming among males created the greatest displacement in physically active leisure. More sedentary adolescents not only engaged in less physically active leisure but when they did participate they were less likely to experience intrinsically rewarding flow, and consequently, the psychological growth opportunities it provides.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".