State-of-the-art literature review of Recovery College evaluative studies between 2013-2024

Over the past ten years, the Recovery College (RC) practice model has spread at an incredible speed. After ten years of implementation and evaluative research on RC, it seemed worthwhile to analyze the state-of-the-art of these evaluative studies. The aim of this literature review is to provide a systematic analysis answering the questions: 1) Since the first evaluative studies of RC, how have RC studies been developed, implemented and evaluated between 2013-2024? 2) What are the findings and gaps in the studies published between 2013-2024?

A state-of-the art literature review was conducted with no date limits on peer-review articles in MEDLINE and Scopus electronic databases. The good practice guide for a systematic literature review published by Siddaway et al. was used, in combination with a structured multi-stage process. Endnote, Covidence and NVivo softwares were used to collect relevant evaluative studies, screen them based on blind selection, analyze their content and ensure inter-rater validation. The quality of each study was assessed using the Kmet grids by two independent assessors.

A total of 64 articles published between January 2013 and June 2024 were selected. Analysis of these articles revealed five qualitative clusters. Early articles on the RC focused on implementation stages and lessons (2013-2024). Next, articles focused on perceived benefits, learners' experience and active ingredients (2014-2024). Articles then moved on to outcomes evaluation (2015-2024) and service utilization and costs (2019-2024). Finally, articles focused on documenting an international scope of the RC and providing a status report and global multicenter comparisons (2019-2023).

These groups of articles capture the scope and richness of the studies, but also the progression in study quality over the past 10 years. To keep pace with this progression, future studies need to consolidate outcome measurement and sustainability over time, using models with high statistical power. Thus, they need to move to crossover designs and randomized controlled trials and give preference to multicenter, international studies with high statistical power.