Emergent patterns of risk for psychopathology: The influence of infant avoidance and maternal caregiving on trajectories of social reticence
Bibliographic record
Abstract
The current study investigated the influential role of infant avoidance on links between maternal caregiving behavior and trajectories at risk for psychopathology. A sample of 153 children, selected for temperamental reactivity to novelty, was followed from infancy through early childhood. At 9 months, infant avoidance of fear-eliciting stimuli in the laboratory and maternal sensitivity at home were assessed. At 36 months, maternal gentle discipline was assessed at home. Children were repeatedly observed in the lab with an unfamiliar peer across early childhood. A latent class growth analysis yielded three longitudinal risk trajectories of social reticence behavior: a high-stable trajectory, a high-decreasing trajectory, and a low-increasing trajectory. For infants displaying greater avoidance, 9-month maternal sensitivity and 36-month maternal gentle discipline were both positively associated with membership in the high-stable social reticence trajectory, compared to the high-decreasing social reticence trajectory. For infants displaying lower avoidance, maternal sensitivity was positively associated with membership in the high-decreasing social reticence trajectory, compared to the low-increasing trajectory. Maternal sensitivity was positively associated with the high-stable social reticence trajectory when maternal gentle discipline was lower. These results illustrate the complex interplay of infant and maternal behavior in early childhood trajectories at risk for emerging psychopathology.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".