Integrating patient and public involvement into co-design of healthcare improvement: a case study in maternity care
Bibliographic record
Abstract
BACKGROUND: Despite recognition of the importance of patient and public involvement (PPI) in healthcare improvement, compelling examples of "what good looks like" for PPI in co-design of improvement efforts, how it might be done, and formalisation of methods and reporting are lacking. In this article, we sought to address these gaps through a case study to illustrate a principled approach to integrating PPI into the co-design of healthcare improvement. METHODS: The case study aimed to involve maternity service users in the co-design of clinical resources for a maternity improvement programme, using a four-stage approach: 1) establishing guiding principles for PPI in the programme, 2) structuring PPI for the programme, 3) co-designing improvements with PPI, and 4) seeking feedback on PPI in the co-design process. RESULTS: Partnership-focused frameworks and other literature on PPI and co-design informed the guiding principles. The structure included a five-member PPI group who provided continuous input, and an additional 15-member PPI group who met twice to discuss experiences of obstetric emergency. PPI in the co-design processes shaped the development of the resources in multiple ways, such as strengthening the prominence given to listening to those in labour and their birth partners, ensuring inclusivity of visuals and language, and developing communication principles informing all resources. Feedback suggested that PPI members felt valued, listened to, and supported to provide unanticipated contributions. CONCLUSIONS: The case study demonstrated how a principled approach to PPI enabled service users to play a key role in co-design of clinical resources aimed at improving the quality and safety of maternity care in the UK. Further case studies, across different clinical areas and with varying levels of resources, are needed to validate this approach.
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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 | 0.049 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| 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".