Barriers and Facilitators to Implementing Attachment-based Family Therapy into a Child Welfare Setting: A Qualitative Process Evaluation

Child welfare systems (CWSs) worldwide show increased interest in adopting empirically informed clinical strategies to increase treatment effectiveness. Many empirically supported treatments (ESTs) exist, but little is known about EST implementation barriers and facilitators in CWS. This study explored CWS providers’ experiences of implementing attachment-based family therapy (ABFT) in home-based services of the Flemish CWS (in Belgium). Sixteen CWS providers (twelve counselors and four supervisors) involved in three home-based services were interviewed. The Consolidated Framework for Implementation Research (CFIR) was used to guide collection, coding, and analysis of interview data. Findings revealed that implementation success was related to ABFT's fit with the CWS's mission, philosophy, and existing practices. CWS providers’ belief in the compatibility between ABFT and CWS increased investment in implementation efforts and persistence to overcome challenges and setbacks. Some barriers pertained to the learning of ABFT and some barriers pertained to systems level challenges such as lack of leadership and support, poor coordination with referral sources and other youth care partners, and lack of policy support. For successful expansion of ESTs into CWS settings, various barriers at multiple systemic levels need to be addressed.     

Illusory line linking solid rods

A solid object--a frame enclosing rods--can be seen as having an illusory 'line' joining the tips of the rods.     

Incorporating Demographic Embeddings Into Language Understanding

Meaning depends on context. This applies in obvious cases like deictics or sarcasm as well as more subtle situations like framing or persuasion. One key aspect of this is the identity of the participants in an interaction. Our interpretation of an utterance shifts based on a variety of factors, including personal history, background knowledge, and our relationship to the source. While obviously an incomplete model of individual differences, demographic factors provide a useful starting point and allow us to capture some of this variance. However, the relevance of specific demographic factors varies between situations—where age might be the key factor in one context, ideology might dominate in another. To address this challenge, we introduce a method for combining demographics and context into situated demographic embeddings—mapping representations into a continuous geometric space appropriate for the given domain, showing the resulting representations to be functional and interpretable. We further demonstrate how to make use of related external data so as to apply this approach in low‐resource situations. Finally, we show how these representations can be incorporated into improve modeling of real‐world natural language understanding tasks, improving model performance and helping with issues of data sparsity.