Stories for Change: The impact of Public Narrative on the co‐production process
Bibliographic record
Abstract
INTRODUCTION: Involving service users in health service design and delivery is considered important to improve the quality of healthcare because it ensures that the delivery of healthcare is adapted to the needs of the users. Co-production is a process used to involve service users, but multiple papers have highlighted the need for the mechanisms and values guiding co-production to be more clearly stated. The aim of this paper was to evaluate the mechanisms and values that guided the co-production approach of the Stories for Change project, which used Public Narrative as part of the co-design process to create change in National Health Service maternity services. METHODS: This study was conducted using a rapid feedback evaluation approach. Semistructured interviews (n = 16) were the main source of data, six of which were maternity service users, with observations (5 h) and documentary analysis also carried out in parallel. RREAL sheets were used for data analysis to organize data based on key topics of interest. RESULTS: This study identified three broad mechanisms and values underpinning the co-production approach: creating an open and safe space to share ideas, learning how to tell stories using Public Narrative and having service providers who play a key role in strengthening the health system listen to stories compelling them to action. This study identified the main areas for improvement of the Stories for Change project related to recruitment, the inclusion of participants, the co-design process, the Skills Session and the Learning Event. CONCLUSION: Our study provided a deeper understanding of the co-production approach that addresses the need to uncover the mechanism and values underlying co-production and co-design approaches. This study expands on the literature pertaining to the influence of storytelling in creating meaningful change in health care. We propose a co-design methodology that uses Public Narrative as a model for service user engagement to help inform future healthcare development processes. PATIENT OR PUBLIC CONTRIBUTION: The experiences and perceptions of maternity service users and health professionals informed this evaluation. The project organizers were involved in the manuscript preparation stage by providing feedback, and service users wrote a commentary on the project from the lived experience perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.071 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".