A Hybrid Machine Learning Framework for Predicting Emotional Reactivity in Adolescents Using Neurocognitive and Environmental Factors
Bibliographic record
Abstract
Objective: The objective of this study was to develop and validate a hybrid machine learning framework capable of predicting individual differences in adolescent emotional reactivity by integrating neurocognitive indicators and environmental factors within a community-based adolescent sample. Methods and Materials: This cross-sectional study was conducted among adolescents aged 12–18 years recruited from secondary schools in Germany using a multistage cluster sampling approach. Participants completed a comprehensive assessment battery including standardized neurocognitive tasks measuring inhibitory control, working memory, cognitive flexibility, and attentional regulation, alongside validated self-report instruments assessing emotional reactivity and key environmental variables such as family emotional climate, parenting consistency, peer stress exposure, school climate, and perceived social support. Data preprocessing included missing-value imputation, feature scaling, and feature engineering. Multiple machine learning algorithms, including regularized regression, tree-based ensemble models, and kernel-based methods, were trained and integrated into a hybrid ensemble using a meta-learning strategy. Model performance was evaluated using nested cross-validation procedures. Findings: Inferential analyses demonstrated that the hybrid ensemble model significantly outperformed individual models, achieving lower prediction error and higher explained variance in emotional reactivity outcomes. Environmental factors, particularly peer stress exposure and family emotional climate, accounted for the largest proportion of predictive importance, followed by neurocognitive variables, with socio-demographic characteristics contributing comparatively less. Deficits in inhibitory control and cognitive flexibility were positively associated with emotional reactivity, whereas supportive family environments and higher perceived social support were associated with reduced emotional reactivity. Conclusion: The findings indicate that adolescent emotional reactivity is best understood as an emergent outcome of interacting neurocognitive and environmental systems and that hybrid.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".