Early Temperament Prospectively Predicts Anxiety in Later Childhood
Bibliographic record
Abstract
OBJECTIVE: To investigate the contribution of early childhood temperamental constructs corresponding to 2 subtypes of general negative emotionality-fearful distress (unadaptable temperament) and irritable distress (fussy-difficult temperament)-to later anxiety in a nationally representative sample. METHOD: Using multiple linear regression analyses, we tested the hypothesis that caregiver-reported child unadaptable temperament and fussy-difficult temperament scales of children aged 2 to 3 years (in 1995) would prospectively predict caregiver-reported child anxiety symptoms at ages 4 to 5, 6 to 7, 8 to 9, and 10 to 11 years, and child-reported anxiety at 10 to 11 years (controlling for sex, age, and socioeconomic status) in a nationally representative sample from Statistics Canada's National Longitudinal Survey of Children and Youth (initial weighted n = 768,600). RESULTS: Only fussy-difficult temperament predicted anxiety in children aged 6 to 7 years. In separate regressions, unadaptable temperament and fussy-difficult temperament each predicted anxiety at 8 to 9 years, but when both were entered simultaneously, only unadaptable temperament remained a marginal predictor. Temperament did not significantly predict caregiver- or child-reported anxiety at 10 to 11 years, suggesting that as children age, environmental factors may become more important contributors to anxiety than early temperament. CONCLUSION: Our results provide the first demonstration that early temperament is related to later childhood anxiety in a nationally representative sample.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".