Longitudinal Predictors of Self-Regulation at School Entry: Findings from the All Our Families Cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Self-regulation is the ability to manage emotions, modulate behaviors, and focus attention. This critical skill begins to develop in infancy, improves substantially in early childhood and continues through adolescence, and has been linked to long-term health and well-being. The objectives of this study were to determine risk factors and moderators associated with the three elements of self-regulation (i.e., inattention, emotional control, or behavioral control) as well as overall self-regulation, among children at age 5. Participants were mother–child dyads from the All Our Families study (n = 1644). Self-regulation was assessed at age 5. Risk factors included income, maternal mental health, child sex, and screen time, and potential moderation by parenting and childcare. Adjusted odds ratios of children being at risk for poor self were estimated using multivariable logistic regression. Twenty-one percent of children had poor self-regulation skills. Risk factors for poor self-regulation included lower income, maternal mental health difficulties, and male sex. Childcare and poor parenting did not moderate these associations and hostile and ineffective parenting was independently associated with poor self-regulation. Excess screen time (>1 h per day) was associated with poor self-regulation. Self-regulation involves a complex and overlapping set of skills and risk factors that operate differently on different elements. Parenting and participation in childcare do not appear to moderate the associations between lower income, maternal mental health, male sex, and screen time with child self-regulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it