The ATTACH™ program and immune cell gene expression profiles in mothers and children: A pilot randomized controlled trial
Bibliographic record
Abstract
Children exposed to adversity and toxic stress are at increased risk for poor health across the lifespan, possibly through alterations to immune pathways. Parenting interventions could buffer the effect of adversity on child immune activity. The purpose of this study was to test whether mothers and children who were randomly assigned to a parenting intervention (ATTACH™) had healthier post-intervention immune cell gene expression patterns, as indexed by the Conserved Transcriptional Response to Adversity (CTRA), compared with mothers and children in a wait-list control group. A sample of 20 mother-child dyads were recruited from a domestic violence shelter in Calgary, AB, Canada. The ATTACH™ program is a 10-week psycho-educational intervention that fosters maternal reflective function, i.e. how to understand and respond to mental states. Dyads were randomly assigned to an intervention or wait-list group. Dried blood spots were collected from both groups post-intervention, subjected to RNA sequencing, and assessed for CTRA gene expression using mixed effect linear model analysis. Covariates were age, child sex, maternal race/ethnicity, and maternal medication use. In unadjusted models, differences by treatment group were detected, F(1,1794) = 4.26, p = .039. Mothers and children who completed the ATTACH™ intervention had lower CTRA scores, indicating healthier immune cell gene expression profiles (Mn = −0.36, SE = 0.17), compared with mothers and children in the wait-list control group (Mn = 0.11, SE = 0.15). Results persisted after controlling for covariates. ATTACH™ participation predicted healthier immune cell gene expression profiles post-intervention compared with wait-list controls. Parenting interventions could decrease the impact of toxic stress on maternal-child immune health.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".