Co-designing peer-to-peer support in oncology: A participatory study on the development of the PaRole OncO France model
Bibliographic record
Abstract
OBJECTIVES: Accompanying Patients (APs) are individuals with lived experience of cancer who provide emotional, informational, and navigational support to patients. However, peer-to-peer support interventions in oncology remain inconsistently developed and rarely integrated into clinical practice in France. This study aims (1) to co-design a context-sensitive peer-to-peer support intervention for oncology units, (2) to identify institutional enablers and barriers to implementation, and (3) to develop tailored implementation pathways using implementation science frameworks. METHODS: A multi-site participatory study was conducted in nine oncology units across four French healthcare institutions. Eight patient partners were involved as co-researchers contributing to study design, facilitation of co-design workshops, and iterative model refinement. A structured co-design methodology guided four interactive workshops per unit. Data were collected through workshop materials, observation notes, and co-researcher reflections, and analyzed thematically using the Consolidated Framework for Implementation Research (CFIR) framework, and the resulting intervention was described using the Template for Intervention Description and Replication (TIDieR). RESULTS: Twenty workshops involving 60 stakeholders (APs, healthcare professionals and managers) resulted in the co-construction of a peer-to-peer support model aligned with local care structures. Three key outputs emerged: (1) a shared definition of the APs role, including expected psychosocial competencies and training needs; (2) identification of institutional enablers and barriers to implementation, such as leadership support, physical environment constraints, and role legitimacy; and (3) tailored implementation pathways, including onboarding, supervision, and integration into care processes. The resulting PaRole OncO France (PROOF) model was designed to be adaptable while preserving core components. CONCLUSION: This study demonstrates the feasibility and value of co-designing a peer-to-peer support intervention in oncology, grounded in lived experience and local context. PRACTICE IMPLICATIONS: The PROOF model offers a replicable framework for integrating APs into cancer care teams. Findings provide actionable guidance for institutions seeking to implement sustainable, patient-partnered peer-to-peer support programs.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".