Preparing psychiatric nurses for mHealth adoption in mental healthcare: A qualitative study of stakeholders'...

This study explored stakeholders' sense-making during the pre-implementation phase of a nationally scalable mHealth intervention in mental healthcare, using the SAFE app as a case example. A qualitative design was employed, which combined individual interviews and focus groups with key stakeholders involved in mental healthcare and digital health initiatives. The data were transcribed verbatim and analysed using reflexive thematic analysis. Four interrelated themes were generated. Navigating the Digital Jungle captured concerns about the proliferation of mental health apps and the lack of clear regulatory standards, which impeded awareness, trust and informed decision-making. Negotiating Relevance reflected how mHealth adoption considerations were shaped by perceived relevance in relation to end-user needs, clinical context and professional or peer endorsement. Reconsidering Traditional Mindsets highlighted hesitancy linked to entrenched professional identities, established practices and change fatigue within mental healthcare settings. Fostering Shared Awareness emphasised the importance of collaborative sense-making, multimodal communication and organisational support in strengthening early mHealth adoption readiness. The findings demonstrate that readiness to adopt mHealth interventions is not a linear or individual process; readiness is shaped through relational, professional, and organisational dynamics. Trust, perceived relevance, the context, and facilitation emerged as interdependent conditions influencing early considerations regarding mHealth adoption. A novel contribution of this study is the identification of adoption readiness as a relational and collective process that develops during the pre-implementation phase before implementation strategies are formally initiated. Early engagement of psychiatric nurses and other key stakeholders is therefore critical to support credible, equitable and context-sensitive implementation.