Household Social Needs, Emotional Functioning, and Stress in Low-Income Latinx Children and their Mothers
Bibliographic record
Abstract
Latinx families may be particularly vulnerable to emotional dysfunction, due to higher rates of economic hardship and complex social influences in this population. Little is known about the impact of environmental stressors such as unmet social needs and maternal stress on the emotional health of Latinx children from low-income families. We conducted secondary analyses using survey and biomarker data from 432 Latinx children and mothers collected in a separate study. We used binomial and multinomial logistic regression to test if household social needs, or maternal perceived stress or hair cortisol concentration (HCC), predicted child measures of emotional functioning or child HCC, independent of relevant sociodemographic factors. Approximately 40% of children in the sample had symptoms consistent with emotional dysfunction, and over 37% of households reported five or more social needs. High perceived maternal stress predicted higher odds of child emotional dysfunction (OR = 2.15; 95% CI [1.14, 4.04]; p = 0.01), and high maternal HCC was positively associated with high child HCC (OR = 10.60; 95% CI [4.20, 26.74]; p < 0.01). Most individual household social needs, as well as the level of household social need, were not independently associated with child emotional dysfunction or child HCC. Our findings begin to define a framework for understanding emotional health, stress, and resilience when caring for Latinx children and mothers living with high levels of social need, and the need for integrated mental health and social needs screening and interventions in settings that serve this population.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".