93 Risk and protective factors for self-regulation at school entry
Bibliographic record
Abstract
Self-regulation, defined as the ability to manage emotions, regulate behaviors, and focus attention, are developed early in childhood and provide the underpinnings for social, emotional, academic, and behavioural well-being in childhood. Early identification of factors associated with poor-self regulation can help inform interventions to promote school readiness. What factors are associated with poor self-regulation among children at age 5? A total of 1688 women participating in a longitudinal pregnancy cohort study in a Canadian city completed questionnaires from pregnancy up to when their children were five years of age. Self-regulation was measured at age 5 by maternal report on the Behavioural Assessment Scale for Children-2 (BASC-2). Children who scored “at risk” on the attention, executive function or emotion control subscales were considered to be at risk for poor self-regulation. Risk and protective factors included maternal and child characteristics, maternal mental health, and family environment, measured at age 3. Multivariable logistic regression models were used to estimate adjusted odd ratios (AOR) for poor self regulation. Twenty-one percent of children had lower self-regulation skills, and this was higher among boys (24%) compared to girls (19%). Risk factors for poor self-regulation included lower family income (AOR 1.45 95% CI 1.07, 1.95), mothers with a history of adverse childhood experiences (AOR 1.53, 95%CI 1.08, 2.14), and mothers with lower emotional stability (AOR 2.16 95% CI 1.57, 2.97). Protective factors included high levels of social support (0.65, 95%CI 0.47, 0.88). Several parenting styles were assessed, and only children of mothers who engaged in more hostile parenting behaviours were more likely to have poor self-regulation skills at age five (AOR: 3.00 95%CI: 2.21, 4.06). Children who watched more than 1 hour of television per day had a 1.35 increased odds (95% CI: 1.04, 1.73) of poor self-regulation. Children are at risk of lower self-regulation due to both family circumstances (lower income) as well as maternal experiences and emotional instability. Hostile parenting puts children at particular risk for poor self-regulation and strategies that support alternate parenting approaches could be beneficial. Finally, monitoring and managing screen time may allow children to engage in other opportunities that foster self-regulation skills.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".