Predicting children's internalizing symptoms across development from early emotional reactivity
Bibliographic record
Abstract
Abstract Current methods of assessing children's emotional reactivity fail to capture individual differences in emotion across contexts that may be meaningfully related to youth psychopathology. We therefore explored the utility of modeling variability in young children's positive and negative emotion across emotionally evocative laboratory tasks to predict later adjustment. At age 3, 409 children completed a battery of laboratory tasks eliciting either positive or negative affect. We used latent difference score (LDS) modeling to predict children's caregiver‐reported internalizing symptoms across ages 3, 5, 8, and 11 from variability in their observer‐rated positive and negative emotion across laboratory tasks. We found that sex moderated the association between both average and variability measures of children's negative emotion at age 3 and trajectories of their anxious‐depressive symptoms across childhood. Measures of emotion variability predicted children's internalizing symptoms above and beyond measures of average emotion. Variability indices also provided unique information about the trajectories of children's symptoms. We discuss implications for the utility of LDS modeling in assessing children's emotional reactivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".