Overcoming training bottlenecks: mixed-methods evaluation of digital training for non-specialists in postnatal depression self-help treatment

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Abstract

Objectives: This mixed-methods study evaluated the feasibility, acceptability and preliminary effectiveness of an interactive digital training programme for non-specialist supporters providing a guided self-help intervention for postnatal depression (PND). Methods: A total of 49 non-specialist trainees participated. Six digital training modules were flexibly delivered over a 5-week period. Training included a chatroom, moderated by a supervised assistant psychologist. Quantitatively, feasibility was assessed via participation and retention levels; acceptability was examined using course evaluation questionnaires; and effectiveness was measured pre-test-post-test quantitatively using a self-report questionnaire and pre-post using scenario questions. Participant focus groups explored feasibility, acceptability and effectiveness. Results: The training was feasible; 41 completed the course and 42 were assessed at follow-up. Quantitative course evaluation and thematic analysis of focus group feedback demonstrated high training acceptability. RANOVAs indicated training significantly improved knowledge and confidence pre- to post-test. There were demonstrable increases in specific skills at post-test as assessed via clinical scenarios. Conclusion: This training is a feasible, acceptable and effective way to upskill non-specialists in supporting treatment for PND, however supervised practice is recommended to ensure participants embed knowledge competently into practice. The training offers an effective first step in upskilling non-specialist supporters to support women with PND treatment at scale.

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APA

Dallison, S., Jenkinson, C., Davey, A., & O’Mahen, H. (2023). Overcoming training bottlenecks: mixed-methods evaluation of digital training for non-specialists in postnatal depression self-help treatment. Journal of Reproductive and Infant Psychology. https://doi.org/10.1080/02646838.2023.2280714

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