Conduct problems, hyperactivity, and screen time among community youth: can mindfulness help? an exploratory study
Bibliographic record
Abstract
Background The influence of mindfulness-based intervention (MBI) programs on behavioural problems among community youth is largely understudied. While technology continues to evolve and the prevalence of screen-based activities is rising, limited studies have accounted for screen time when examining the efficacy of an MBI. Accordingly, this study investigated the impact of MBI on conduct problems and hyperactivity among community youth, accounting for sociodemographic characteristics and four types of screen time. Method Linear regression models were used to investigate 1) the association between four types of screen time and behavioural problems (i.e., conduct problems and hyperactivity) and 2) the efficacy of online mindfulness programs in reducing behavioural problems among community youth. The data were collected at baseline, intervention completion and 1-month follow-up (Spring 2021 to Spring 2022) in Ontario, Canada ( n =117, mean age=16.82, male=22%, non-White=21%). Results The average score for conduct problems was within the normal range, while the average score for hyperactivity was considered borderline at baseline. Accounting for other types of screen time, time spent playing video games was significantly associated with increased conduct problems ( β = 1.75, p =.03), albeit rendering non-significant after correcting for multiple comparisons. The online mindfulness program was significantly associated with reduced hyperactivity, controlling for baseline mental health, age, sex and screen time. Conclusion The current findings suggest a 12-week online mindfulness program may play a positive role in reducing hyperactivity even when accounting for screen time. Our findings advocate the evidence base on the efficacy of MBI in managing hyperactivity.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".