Temperamental, Parental, and Contextual Contributors to Early‐emerging Internalizing Problems: A New Integrative Analysis Approach
Bibliographic record
Abstract
Abstract This study evaluated a comprehensive model of factors associated with internalizing problems (IP) in early childhood, hypothesizing direct, mediated, and moderated pathways linking child temperamental inhibition, maternal overcontrol and rejection, and contextual stressors to IP. In a novel approach, three samples were integrated to form a large sample (N=500) of Canadian children (2–6 years; M=3.95 years; SD=.80). Items tapping into the same constructs across samples were used to create parallel measures of inhibited temperament, maternal positive, critical, and punitive parenting, maternal negative emotionality, family socioeconomic and structural stressors, and child's IP. Multiple‐groups structural equation modeling indicated that associations were invariant across samples and did not differ for boys and girls. Child inhibition, less positive and more critical parenting, maternal negative emotionality, and family socioeconomic disadvantage were found to have direct associations with IP. In addition, maternal negative emotionality was associated with IP through more critical parenting, and both maternal negative emotionality and socioeconomic stress were associated with IP through less positive parenting. Results highlight the multiple independent and cumulative risk factors for early IP and demonstrate the power of integrating data across developmental studies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".