Child Distress Expression and Regulation Behaviors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The goal of the current study was to review and meta-analyze the literature on relationships between child distress expression behaviors (e.g., cry) and three clusters of child distress regulation behaviors (disengagement of attention, parent-focused behaviors, and self-soothing) in the first three years of life. This review was registered with PROSPERO (CRD42020157505). Unique abstracts were identified through Medline, Embase, and PsycINFO (n = 13,239), and 295 studies were selected for full-text review. Studies were included if they provided data from infants or toddlers in a distress task, had distinct behavioral measures of distress expression and one of the three distress regulation clusters, and assessed the concurrent association between them. Thirty-one studies were included in the meta-analysis and rated on quality. Nine separate meta-analyses were conducted, stratified by child age (first, second, and third year) and regulation behavior clusters (disengagement of attention, parent-focused, and self-soothing). The weighted mean correlations for disengagement of attention behaviors were −0.28 (year 1), −0.44 (year 2), and −0.30 (year 3). For parent-focused behaviors, the weighted mean effects were 0.00 (year 1), 0.20 (year 2), and 0.11 (year 3). Finally, the weighted mean effects for self-soothing behaviors were −0.23 (year 1), 0.25 (year 2), and −0.10 (year 3). The second year of life showed the strongest relationships, although heterogeneity of effects was substantial across the analyses. Limitations include only analyzing concurrent relationships and lack of naturalistic distress paradigms in the literature.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".