Comment impliquer le public et les patient(e)s dans la recherche en prévention primaire: Perspectives internationales et leçons tirées du réseau de recherche en prévention primaire des cancers CANCEPT
Bibliographic record
Abstract
OBJECTIVES: This article explores the methods and benefits of involving patients and the public in primary prevention (PP) cancer research. Through the experience of the CANCEPT PP research network, it aims to clarify practices and propose methodological guidelines for developing interventions tailored to the needs of populations. METHODS: The Methodological Exchange Group GEM MIXTE, made up of 9 researchers and 9 co-researchers, met monthly (2023-2024, France) in order to define public and patient involvement in PP, take stock of participatory practices among CANCEPT members via a questionnaire (14 researchers, 8 institutions, 22 projects), and develop methodological guidelines in the form of a mind map. The participatory approaches were evaluated using the Public and Patient Engagement Evaluation Tool (PPEET) questionnaire. RESULTS: GEM MIXTE has enabled us to clarify the methodology of participatory research in PP and to propose practical guidelines. This work highlights the importance of diversifying the profiles of the co-researchers to enhance the relevance of interventions, while emphasizing the role of the group coordinator in structuring and facilitating exchanges. The PPEET evaluation confirmed that the co-researchers were committed to the objectives, although socio-cultural diversity remains a challenge. CONCLUSION: This work proposes a methodological framework for integrating lived experience into research. The guidelines developed provide tools to inspire other networks to structure their approaches effectively, thereby strengthening citizen participation in public health.
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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 | 0.111 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".