Early childhood risk and resilience factors for behavioural and emotional problems in middle childhood
Bibliographic record
Abstract
BACKGROUND: Mental disorders in childhood have a considerable health and societal impact but the associated negative consequences may be ameliorated through early identification of risk and protective factors that can guide health promoting and preventive interventions. The objective of this study was to inform health policy and practice through identification of demographic, familial and environmental factors associated with emotional or behavioural problems in middle childhood, and the predictors of resilience in the presence of identified risk factors. METHODS: A cohort of 706 mothers followed from early pregnancy was surveyed at six to eight years post-partum by a mail-out questionnaire, which included questions on demographics, children's health, development, activities, media and technology, family, friends, community, school life, and mother's health. RESULTS: Although most children do well in middle childhood, of 450 respondents (64% response rate), 29.5% and 25.6% of children were found to have internalising and externalising behaviour problem scores in the lowest quintile on the NSCLY Child Behaviour Scales. Independent predictors for problem behaviours identified through multivariable logistic regression modelling included being male, demographic risk, maternal mental health risk, poor parenting interactions, and low parenting morale. Among children at high risk for behaviour problems, protective factors included high maternal and child self-esteem, good maternal emotional health, adequate social support, good academic performance, and adequate quality parenting time. CONCLUSIONS: These findings demonstrate that several individual and social resilience factors can counter the influence of early adversities on the likelihood of developing problem behaviours in middle childhood, thus informing enhanced public health interventions for this understudied life course phase.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".