Exploring Parent-Child Dyadic Networks to Design a Smartphone-based Mindfulness Intervention for Underserved Families
Bibliographic record
Abstract
Parents from socioeconomically disadvantaged and minoritized backgrounds disproportionately experience higher rates of daily negative mood and lower levels of physical activity, negatively impacting health outcomes across the lifespan. Scalable, evidence-based interventions to promote family wellbeing and that fit into the busy lives of parents juggling many responsibilities remain beyond reach, especially for families from minoritized groups experiencing the greatest barriers to health. Mindfulness holds promise for addressing these disparities in access to evidence-based behavioral health interventions because it can be engaged upon at any given moment, can be delivered using smartphones, has higher rates of program completion relative to other evidence-based treatments, and has high rates of being integrated into daily life following study completion. In this study, we used smartphone experience-sampling and accelerometry to respectively measure mood and physical activity in 31 parent-child dyads. Parents reported negative mood and stress 10 times per day across 14 days for themselves and their child (3-8 years old). We used Group Iterative Multiple Model Estimation to construct networks describing associations among negative mood, stress, and physical activity for each parent and child dyad. Our findings suggest that we can use a complex systems approach to model the associations among mood, stress, and physical activity in parents and children in daily life. We discuss opportunities for using these parent-child dyad networks to inform the design of parent focused smartphone-based mindfulness interventions to support healthy parent-child interactions and reduce health disparities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".