Partnering with Patients and the Public in the Clinical Trials Ecosystem: A Decade of Lessons Learned and Shared
Notice bibliographique
Résumé
BACKGROUND: In the health research space, it is becoming more common to see patient and public partners as members of teams and contributing to various initiatives rather than solely as participants in research. We use the term patient and public engagement (PPE) to describe this approach. While reporting mechanisms such as the Guidance for Reporting Involvement of Patients and the Public, Version 2 (GRIPP2), are being used to share teams' PPE efforts related to specific research projects, to date we have seen less about how organisations have collaborated with patient and public partners on a broader scale. Clinical Trials Ontario (CTO) is a not-for-profit organisation in the clinical trials space that has been set up to improve the environment for trials rather than conduct trials. We share CTO's PPE processes, how they have evolved over the past decade, outputs related to this work, and lessons learned. METHODS: Patient and public partners and team members from CTO used a co-production model. We have co-developed PPE processes to guide and carry out the work under CTO's strategic pillar of Engage. The Patient and Public Engagement Evaluation Tool has played a role in helping ensure CTO's approaches to co-production are on track. Feedback and insights provided in the evaluation tool after engagements have resulted in iterative changes to this work. RESULTS: CTO has engaged patient and public partners in its work since its inception in 2014. Gradually, this work has evolved based on both the needs of CTO and the patient and public partners with whom it collaborates. Originally, a small group called the patient and public advisory group was created, which met quarterly in person and provided guidance related to specific products (eg, CTO's website about clinical trials, a clinical trial finder). Based on lessons learned with that group and the abilities of people to participate, the group was expanded to become CTO's College of Lived Experience. The College of Lived Experience includes more perspectives and meets more frequently and predominantly virtually, having been formed just before the COVID-19 pandemic was declared. Working with the College has resulted in: members being embedded in a range of CTO projects, a number of public-facing outputs and generating a research idea and carrying out a project that resulted in a patient partner co-authored, peer-reviewed publication. The College is also available to and has provided input and insights into projects and initiatives outside of CTO. We share lessons learned from organisational perspectives and from those College members who are co-authors of this work. CONCLUSIONS: CTO's work in PPE has resulted in a co-production model that is critical to efforts in CTO's Engage strategic pillar and beyond. We provide templates and outputs of this co-production work and lessons learned. We hope our work helps other clinical trials organisations across the globe to include patient and public partners in their operational efforts.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,377 | 0,261 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,006 | 0,008 |
| Études des sciences et des technologies | 0,015 | 0,054 |
| Communication savante | 0,058 | 0,072 |
| Science ouverte | 0,011 | 0,052 |
| Intégrité de la recherche | 0,017 | 0,046 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».