Family Systems Cultural and Resilience Dimensions to Consider in Nutrition Interventions: Exploring Preschoolers’ Eating and Physical Activity Routines During COVID-19
Bibliographic record
Abstract
OBJECTIVE: To describe the weight-related family functioning of racial minority families with low income using family systems theory as an interpretive framework. DESIGN: Primarily a qualitative study with interviews plus; descriptive demographics, anthropometrics, a family functioning measure, and food insecurity screening. SETTING: Telephone interviews with families of preschool-aged children in an urban setting. PARTICIPANTS: Primary caregivers of preschool-aged children. PHENOMENON OF INTEREST: Cultural impacts on family systems. ANALYSIS: Interviews were audio-recorded, transcribed verbatim, and loaded into NVivo 12 for thematic analysis. Descriptive statistics. RESULTS: The 23 participants were mothers and 2 maternal grandmothers. Seventy-four percent were African American, most children were normal weight (n = 15, 65%), mean family function scores were high, and more than half the families were at risk for food insecurity (n = 13, 56%). Acculturation and intergenerational eating-related cultural dimensions were discerned as the overarching themes influencing family cohesion. Family cohesion appeared to have helped the families adapt to the impact of coronavirus disease 2019. CONCLUSIONS AND IMPLICATIONS: Cultural dimensions such as acculturation and intergenerational influences appeared to be associated with social cohesion and family functioning around weight-related behaviors for these families. These findings add cultural and family resilience dimensions to family systems theory in nutrition interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".