Co-designing social prescribing pathways: guidance on the use of deliberative dialogues for inclusive implementation
Bibliographic record
Abstract
BACKGROUND: Participatory methods can support the co-design and implementation of contextualized social prescribing programs by integrating diverse perspectives into the design process and prioritizing actions for implementation. Deliberative dialogues are a participatory method that provide structured, action-oriented forums for discussion and can promote equitable and impactful engagement in design initiatives. The aim of this work was to describe the use of this method in the co-design of social prescribing programs, providing practical guidance and lessons learned from conducting deliberative dialogues with interest-holders across health and social sectors. METHODS: We conducted initial outreach and held two online deliberative dialogues with local primary health care providers and community organizations. Additionally, in 2024, we hosted an in-person workshop on using deliberative dialogue to co-design social prescribing programs at an international conference on social prescription held in Toronto, Canada. We describe how the deliberative dialogues were facilitated to establish feasible pathways for social prescribing, determine core program components, and identify implementation barriers. RESULTS: Outreach activities and deliberative dialogues with diverse interest-holders helped identify local champions, build connections across sectors, and foster creative ideation. Participants consistently emphasized the need for inclusive, accessible social prescribing programs grounded in holistic, person-centered care. A team-based approach linking health and community services was similarly perceived as essential to ensuring continuity along the social prescribing pathway. Evident in our process and participant comments was the importance of early and meaningful engagement of interest holders in designing responsive and contextually relevant programs. CONCLUSION: Deliberative dialogues offer a participatory, transformative, and pragmatic approach to co-designing social prescribing initiatives, facilitating meaningful collaboration and constructive exchange among diverse interest-holders. The insights and practical suggestions generated through this work can inform future participatory implementation efforts.
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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 | 0.234 | 0.266 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".