What mums think matters: A mediating model of maternal perceptions of the impact of screen time on preschoolers' actual screen time
Bibliographic record
Abstract
Screen time during the preschool years is detrimental to wellbeing. The impact of parental perceptions on preschoolers' screen time is unknown. This paper explores the association between maternal perceptions of the impact of screen time on their preschoolers' wellbeing with their child's screen time and the potential mediating role of their perception of the appropriate amount of screen time. In 2013-2014, mothers of 575 preschoolers (2-5 years; metropolitan Melbourne and online sources) reported: their perceptions of the impact of screen time on 11 aspects of wellbeing, conceptually grouped to physical, social and cognitive well-being; their perceptions of the appropriate amount of screen time for preschoolers; and their child's actual screen time. Regression analyses investigated associations between perceptions and children's screen time. Mediation by perception of the appropriate amount of screen time was examined using indirect effects. Mothers' perceptions of the impact of screen time on social and cognitive wellbeing had a significant indirect effect on children's actual screen time through mothers' perception of the appropriate amount of screen time for their child. Findings illustrate the potential impact of parents' perceptions on their children's behaviors. Although a significant indirect effect was identified, direction of causality cannot be implied. Further exploration of the direction of association to determine causality, and interventions targeting parental perceptions, are warranted.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".