Maternal Psychological Distress and Offspring Psychological Adjustment in Emerging Adulthood: Findings from Over 18 Years
Bibliographic record
Abstract
OBJECTIVE: To examine the long-term prediction of psychological maladaptive (i.e., symptoms of anxiety and depression) and adaptive adjustment (i.e., self-efficacy) in emerging adult offspring from trajectories of maternal psychological distress from toddlerhood to adolescence. METHOD: Trajectories of maternal psychological distress (low, moderate, high, and low-rising patterns) from toddlerhood (age 1.5 years) to adolescence (age 14.5 years) were used to predict psychological adjustment in emerging adult offspring (age 18-20 years) (n = 400). RESULTS: Adverse maternal distress trajectories during childhood were linked to maladaptive and adaptive adjustment in adult offspring. Consistently high maternal distress levels experienced across childhood predicted higher symptoms of anxiety and depression and lower self-efficacy than low maternal distress trajectories. Two other adverse maternal distress trajectories (consistently moderate and low-rising patterns) compared with the low trajectory predicted higher offspring depressive symptoms. The findings persisted when adjusting for potential confounders: offspring gender and maternal education, relationship status, language, and economy. CONCLUSION: The current study showed longitudinal multi-informant impact from adverse maternal distress trajectories to adult offspring maladjustment over 18 years, emphasizing the importance of early identification and prevention.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".