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Background: Children from low-socioeconomic backgrounds exhibit more behavioural difficulties than those from more affluent families. Influential theoretical models specify family stress and child characteristics as mediating this effect. These accounts, however, have often been based on cross-sectional data or longitudinal analyses that do not capture all potential pathways, and therefore may not provide good policy guidance., Methods: In a UK representative sample of 2399 children aged 5-15, we tested mediation of the effect of household income on parent and teacher reports of conduct problems (CP) via unhealthy family functioning, poor parental mental health, stressful life events, child physical health and reading ability. We applied cross-lagged longitudinal mediation models which allowed for testing of reciprocal effects whereby the hypothesised mediators were modelled as outcomes as well as predictors of CP., Results: We found the predicted significant longitudinal effect of income on CP, but no evidence that it was mediated by the child and family factors included in the study. Instead, we found significant indirect paths from income to parental mental health, child physical health and stressful life events that were transmitted via child CP., Conclusion: The results confirm that income is associated with change in CP but do not support models that suggest this effect is transmitted via unhealthy family functioning, parental mental health, child physical health, stressful life events or reading difficulties. Instead, the results highlight that child CP may be a mediator of social inequalities in family psychosocial functioning.

Access to education is the first step to benefiting from it. Although cumulative online learning experience is linked academic learning gains, between-country inequalities mean that large populations are prevented from accumulating such experience. Low-and-middle-income countries are affected by disadvantages in infrastructure such as internet access and uncontextualised learning content, and parents who are less available and less well-resourced than in high-income countries. COVID-19 has exacerbated the global inequalities, with girls affected more than boys in these regions. Therefore, the present research mined online learning data to identify features that are important for access to online learning. Data mining of 54,842,787 initial (random subsample n = 5000) data points from one online learning platform was conducted by partnering theory with data in model development. Following examination of a theory-led machine learning model, a data-led approach was taken to reach a final model. The final model was used to derive Shapley values for feature importance. As expected, country differences, gender, and COVID-19 were important features in access to online learning. The data-led model development resulted in additional insights not examined in the initial, theory-led model: namely, the importance of Math ability, year of birth, session difficulty level, month of birth, and time taken to complete a session., Competing Interests: Conflicts of interestThe author declare no conflict of interests., (© The Author(s) 2022.)