Emerging practice in mental health patient and public involvement research advisory groups: a narrative review
Bibliographic record
Abstract
BACKGROUND: The involvement of experts by experience in health research is a requirement from major funders across the world. One approach is Patient and Public Involvement (PPI) research advisory groups. This narrative review surfaced emerging practices in these groups based upon research papers that evaluated their own activities. METHODS: Papers published between 1/1/ 2014 and 10/1/2024 containing advisory group or related terms in the title or abstract were included. Articles had to focus on mental health and describe the evaluation of PPI research advisory groups. Articles where the PPI research advisory group was not the central focus were excluded. RESULTS: We identified 26 papers. Different terms were used and categorised as: Community Advisory Boards; Stakeholder Groups; PPI Groups; Young People Advisory Groups. There was a growth in literature over time; 42% of articles were published in 2023. Youth involvement was covered in 13 papers (50%). Many included a group member as an author (14 papers). Geographically most studies came from UK (n = 10), followed by Australia (n = 5) and Canada (n = 4). Our analysis identified nine themes and 123 sub-themes under: principles and values; group formation; running groups; facilitators; barriers; impacts on PPI group members; impacts on researchers; impacts on mental health research; recommendations. Commonalities in emerging practices included themes relating to: the importance of communication; an inclusive focus ensuring diversity of perspectives; choice and flexibility in how people are involved; creating safe spaces for involvement work; importance of detailed planning processes; and strategies to address power and hierarchy in research. DISCUSSION AND CONCLUSION: This field of practice is rapidly developing, building on well-established models such as Community Advisory Boards, underpinned by an epistemic justice value base. Our positionality led us to conclude more participatory evaluations from cross cultural partnerships delivering PPI research advisory groups would be beneficial to the mental health research ecosystem. This review would also benefit from an update to capture publications from 2024 onwards. PATIENT AND PUBLIC CONTRIBUTION: The study was conceptualised and delivered by nine people working from a lived experience perspective in peer research and public involvement. All are authors on this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".