‘Life in the age of screens’: parent perspectives on a 24‐h no screen‐time challenge
Bibliographic record
Abstract
Screens have become ubiquitous in modern society. Their use frequently underlies sedentary behaviour, a well-established determinant of obesity. As part of a family oriented clinic offering a 2-year lifestyle program for obese children and youth, we explored parents' experiences with a 24-h no screen-time challenge, an intervention designed to raise awareness of screen-time habits and to help families develop strategies to limit their use. In total, 15 parents representing 13 families participated. A focus group with nine parents and six phone interviews with those who could not join in person were conducted. Interviews were transcribed verbatim and analysed qualitatively. Key elements to successful completion of the 24-h no screen-time challenge emerged, namely: clear rules about permitted activities during the 24-h period; togetherness, i.e. involving all family members in the challenge; and busyness, i.e. planning a full schedule in order to avoid idleness and preclude the temptation to use screens. Our findings suggest that practitioners aiming to increase awareness of screen-time or to limit their use may be more likely to succeed if they include all family members, offer concrete alternatives to screen-based activities and provide tailored strategies to manage discretionary time.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".