Trajectories of anxiety in a population sample of children: Clarifying the role of children's behavioral characteristics and maternal parenting
Bibliographic record
Abstract
This study pursued three goals. The first goal was to explore children's trajectories of anxiety from age 6 to 12 using a representative community sample. The second goal was to assess the link between certain behavioral characteristics assessed in kindergarten (i.e., inattention, hyperactivity, aggressiveness, and low prosociality) and these trajectories. The third goal was to determine whether certain aspects of maternal parenting (i.e., warmth and discipline) could moderate the association between these characteristics and the trajectories of anxiety. A population sample of 2,000 children (1,001 boys, 999 girls) participated in this longitudinal study. Developmental trajectory analyses allowed us to identify four trajectory groups: low, low-increasing, high-declining, and high anxiety groups. Moreover, multinomial logistic regressions revealed a profile of children at risk of developing high anxiety symptoms (i.e., high group), characterized by sociofamily adversity, inattention, and low prosociality in the classroom. Hyperactivity was also found in this profile, but only for children exposed to a mother who showed little affective warmth. Finally, mothers' high level of discipline increased the odds of belonging to the high anxiety group. The results are discussed in relation to studies examining the association among anxiety, behavioral characteristics, and parenting during childhood.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".