Allen, V., Lottridge, D., Merry, S., & Stasiak, K. (2024). Building a digital tool to support focused acceptance and commitment therapy practitioners in New Zealand primary care: A qualitative exploration of user needs to guide software feature development. Journal of Contextual Behavioral Science, 32, 100762. https://doi.org/10.1016/j.jcbs.2024.100762
Mental health service scalability needs to be improved to meet the growing global demand. The scalability of Focused Acceptance and Commitment Therapy (fACT), a popular evidence-based brief behavioural intervention used in primary care contexts, could be improved through the development and implementation of a digital tool that supports practitioners to overcome service-delivery problems within their practice context.
Through semi-structured in-depth interviews with 12 fACT practitioners, we examined the service-delivery problems that they face within the New Zealand primary care context and identified organisational factors which may be contributing to these problems. From these interviews, six key themes emerged: (1) The brief model works for most clients but is not suitable for every client, (2) practitioners often struggle to access culturally appropriate fACT congruent exercises and psychoeducation material, (3) practitioners feel that they need additional training to maintain good model fidelity, (4) short session times can negatively impact model delivery, (5) public health employed practitioners have high workloads and are often unable to refer clients to secondary or crisis services, and (6) fACT practitioners are unable to effectively follow up with their clients post-session.
This study is part of a broader project aimed at developing an adjunctive digital tool to support the delivery of fACT in New Zealand primary care. These interviews will help us to understand the problems practitioners face in this service-delivery context, identify context-specific factors that may be causing these problems, and offer insights into the necessary features of an engaging digital tool designed to improve model scalability.
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