The association between crowding within households and behavioural problems in children: Longitudinal data from the Southampton Women’s Survey
Bibliographic record
Abstract
BACKGROUND: In England, nearly one child in ten lives in overcrowded housing. Crowding is likely to worsen with increasing population size, urbanisation, and the ongoing concerns about housing shortages. Children with behavioural difficulties are at increased risk of mental and physical health problems and poorer employment prospects. OBJECTIVE: To test the association between the level of crowding in the home and behavioural problems in children, and to explore what factors might explain the relationship. METHODS: Mothers of 2576 children from the Southampton Women's Survey population-based mother-offspring cohort were interviewed. Crowding was measured at age 2 years by people per room (PPR) and behavioural problems assessed at age 3 years with the Strengths and Difficulties Questionnaire (SDQ). Both were analysed as continuous measures, and multivariable linear regression models were fitted, adjusting for confounding factors: gender, age, single-parent family, maternal education, receipt of benefits, and social class. Potential mediators were assessed with formal mediation analysis. RESULTS: The characteristics of the sample were broadly representative of the population in England. Median (IQR) SDQ score was 9 (6-12) and PPR was 0.75 (0.6-1). In households that were more crowded, children tended to have more behavioural problems (by 0.20 SDQ points (95% CI 0.08, 0.32) per additional 0.2 PPR, adjusting for confounding factors). This relationship was partially mediated by greater maternal stress, less sleep, and strained parent-child interactions. CONCLUSIONS: Living in a more crowded home was associated with a greater risk of behavioural problems, independent of confounding factors. The findings suggest that improved housing might reduce childhood behavioural problems and that families living in crowded circumstances might benefit from greater support.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".