Predicting children's internalizing symptoms across development from early emotional reactivity
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it