Details

Autor(en) / Beteiligte

• Dietrichson, Jens
• Klokker, Rasmus H.

Titel

Predicting preschool problems

Ist Teil von

• Children and youth services review, 2024-06, Vol.161, p.107655, Article 107655

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •We use register data to predict preschool children’s social-emotional problems.•Machine learning models predicted children’s social-emotional problems poorly.•Using register data to target interventions to individual children is thus difficult.•In contrast, the predicted rank of preschools was close to the observed rank.•Machine learning models can identify preschools in need of extra resources. Being able to predict which children will develop social-emotional problems is important for targeting interventions and efficiently allocating resources to preschools. We used the Strengths and Difficulties Questionnaire (SDQ) and preschool teachers’ assessments of 908 children aged 2–7 in 16 preschools in one Danish municipality, data from administrative registers, and a range of prediction models to examine how well social-emotional problems in preschool be can predicted. Although machine learning models typically make better predictions than linear or logistic regression, no model predicted either child or preschool-level social-emotional problems well. However, using the best-performing machine learning model, we obtained a predicted rank of preschools close to the observed rank (Spearman’s r = 0.69), which improved upon predictions based on income and earlier SDQ measures. Our results indicate that although using register data to target interventions to individual children is difficult, prediction models can improve the identification of preschools in need of extra resources.