Unmet Social Needs and Patterns of Hair Cortisol Concentration in Mother–Child Dyads
Bibliographic record
Abstract
Background Mothers and their children demonstrate dyadic synchrony of hypothalamic–pituitary–adrenal (HPA) axis function, likely influenced by shared genetic or environmental factors. Although evidence has shown that chronic stress exposure has physiologic consequences for individuals—including on the HPA axis—minimal research has explored how unmet social needs such as food and housing instability may be associated with chronic stress and HPA axis synchrony in mother–child dyads. Methods We conducted a secondary analysis of data from 364 mother–child dyads with low-income recruited during a randomized trial conducted in an urban pediatric clinic. We used latent profile analysis (LPA) to identify subgroups based on naturally occurring patterns of within-dyad hair cortisol concentration (HCC). A logistic regression model predicted dyadic HCC profile membership as a function of summative count of survey-reported unmet social needs, controlling for demographic and health covariates. Results LPA of HCC data from dyads revealed a 2-profile model as the best fit. Comparisons of log HCC for mothers and children in each profile group resulted in significantly “higher dyadic HCC” versus “lower dyadic HCC” profiles (median log HCC for mothers: 4.64 vs 1.58; children: 5.92 vs 2.79, respectively; P < .001). In the fully adjusted model, each one-unit increase in number of unmet social needs predicted significantly higher odds of membership in the higher dyadic HCC profile when compared to the lower dyadic HCC profile (odds ratio = 1.13; 95% confidence interval [1.04-1.23]; P = .01). Conclusion Mother–child dyads experience synchronous patterns of physiologic stress, and an increasing number of unmet social needs is associated with a profile of higher dyadic HCC. Interventions aimed at decreasing family-level unmet social needs or maternal stress are, therefore, likely to affect pediatric stress and related health inequities; efforts to address pediatric stress similarly may affect maternal stress and related health inequities. Future research should explore the measures and methods needed to understand the impact of unmet social needs and stress on family dyads.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".