“PPI? That sounds like Payment Protection Insurance”: Reflections and learning from a substance use and homelessness study Experts by Experience group
Notice bibliographique
Résumé
BACKGROUND: Patient and Public Involvement in research is important for citizenship, accountability and transparency, and has the practical benefit of helping to ensure its quality and applicability. Involving members of the public in research is becoming increasingly commonplace, in the UK and internationally. It is essential that public involvement is inclusive of individuals and their diverse life experiences, including challenging experiences that may be associated with stigma and social exclusion. The involvement of people with lived/living experience of substance use and homelessness in research is increasing in response to increased recognition of the importance of inclusion and the benefits conferred to research. MAIN BODY: In this commentary, we share our own experiences of being part of a Patient and Public Involvement group that was convened during a feasibility study of a peer-delivered harm reduction intervention. We are a diverse group but share experience of the field of substance use/homelessness, as people with lived/living experience, and as researchers and practitioners. We share our reflections and learning, as well as offer recommendations for researchers working in our field. Our group worked together to make a positive and deliberate contribution to the study. This did not happen by chance but required the development of mutual trust and respect, with each member having a commitment to support the group for its two-year duration. SHORT CONCLUSION: It is important for researchers to appreciate that meaningful Patient and Public Involvement is very valuable but requires a commitment from all involved. Regarding our field of substance use and homelessness specifically, it is essential that people with these experiences have opportunities to contribute to research and can do so in a meaningful way. People with lived/living experience are able to bring to life the rich tapestry of others' experiences. However, the involvement must be neither tokenistic nor indifferent to the wider challenges common to these experiences.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».