Insights about Screen-Use Conflict from Discussions between Mothers and Pre-Adolescents: A Thematic Analysis
Bibliographic record
Abstract
Digital screens have become an integral part of everyday life. In the wake of the digital swell, pre-adolescents and their parents are learning to navigate seemingly new terrain regarding digital media use. The present study aimed to investigate parent and pre-adolescent perceptions of screen use and the source of conflict surrounding digital media. We employed a qualitative thematic analysis of 200 parent and pre-adolescent dyads discussing screen use. Our analysis showed five overarching themes for screen use perceptions and conflict: screen time, effects of screen use, balance, rules, and reasons for screen use. In contrast to previous studies that mainly focused on parental perceptions, we were also able to shed light on pre-adolescent perceptions of screen use and the difference in opinions with their parents. Furthermore, we found that patterns of the source of screen use conflict were oftentimes rooted in the age-old developmental tug of war between autonomy-seeking pre-adolescents and authority-seeking parents. Though navigating autonomy-granting and seeking behavior is familiar to developmental scientists, negotiating these challenges in a new digital world is unfamiliar. Autonomy support, open dialogue, and playful interaction between parents and children are needed to understand and resolve conflict of digital media use in family contexts.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".