Intervening to shape children’s emotion regulation: A review of emotion socialization parenting programs for young children.
Bibliographic record
Abstract
Emotion regulation patterns are shaped, in part, by the quality of interpersonal interactions during early development. Given the important contribution of caregiver emotion socialization, parenting programs targeting these socialization practices represent an effective method of preventative intervention for childhood difficulties with emotion regulation. This review examines emotion socialization parenting programs that target the development of emotion regulation in young children aged 0-6. Through a systematic search of multiple databases, 1117 articles were retrieved for title, abstract, and full-text screening. After screening, 12 articles remained and were summarized using a narrative approach. Herein, we provide an overview of 3 main intervention frameworks: Tuning in to Kids (TIK), Parent-Child Interaction Therapy-Emotion Development (PCIT-ED), and Emotion Enhanced Triple P (EETP). Overall, these emotion socialization programs have demonstrated efficacy in their ability to improve parenting behaviors related to the coaching of young children's emotion regulation; however, there has been limited examination of their effectiveness in promoting children's emotion regulation. Future research is required to address this limitation, with rigorous parenting intervention studies incorporating valid assessments of children's emotion regulation in order to promote children's development of adaptive emotion regulation. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.004 | 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".