Skip to Main Content
Article navigation
Purpose

This study aims to report on the experience of using a strengths-based, solution-focused methodology for co-producing a gender-informed drug treatment service using an appreciative inquiry (AI) model (Cooperrider and Srivastva, 1987).

Design/methodology/approach

An AI model was used to facilitate a series of six workshops. Participants had a mixture of lived experience (n = 4, experience of accessing drug and alcohol services) and learned experience (n = 3, practitioners from a local drug service), with co-facilitators from Fulfilling Lives Lambeth, Southwark and Lewisham (n = 2). The aim of the workshops was to understand barriers, identify solutions and co-create a service design offer. Data for this paper was collected using a series of focus groups, reflection logs and surveys, which sought to understand participants’ perceptions of using this model and the impact it had on them. Data was analysed manually using coded thematic analysis (Braun and Clarke, 2006).

Findings

Participants successfully co-created a women’s access to drug and alcohol service design and recommendations. Participants found the process of using the model a very positive experience with benefits, including increased self-esteem, group cohesion and balanced power. This study provides evidence of the AI model as an effective, practical tool for co-production work.

Originality/value

This case study considers a shift in approach to co-producing services with both lived and learned experience, which moves away from problem-focused consultations, towards solution-focused co-design. Consequently, providing evidence to support such a change.

Licensed re-use rights only
You do not currently have access to this content.
Don't already have an account? Register

Purchased this content as a guest? Enter your email address to restore access.

Please enter valid email address.
Email address must be 94 characters or fewer.
Pay-Per-View Access
$39.00
Rental

or Create an Account

Close Modal
Close Modal