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Enregistrement W4412727147 · doi:10.2196/75741

Public Involvement in Cancer Research: Collaborative Evaluation Using Photovoice

2025· article· en· W4412727147 sur OpenAlexvenueno aff
Piotr Teodorowski, Melanie McInnes, Glen Dale, Linda Galbraith, Esme Radin, Karen Gold, Erica Gadsby

Notice bibliographique

RevueJMIR Cancer · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueParticipatory Visual Research Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPhotovoicePublic involvementNarrativePublic relationsProcess (computing)Public healthMedical educationPolitical sciencePsychologyMedicineNursingComputer science

Résumé

récupéré en direct d'OpenAlex

Background: A public involvement group consisting of 4 public contributors with lived experience of cancer diagnosis contributed to 2 cancer research projects that focused on optimizing the diagnostic pathways for patients with suspected cancer. The public contributors have been involved from the start of the projects and were involved in aspects of the design, analysis, and dissemination alongside research and clinical teams. Despite public involvement in cancer research being seen as a key element of the research process, there is still a limited understanding of what works well and how to do it in a meaningful way for both researchers and public contributors. Objective: This study aims to evaluate the public involvement process in 2 cancer research projects. Methods: This was a collaborative evaluation with the research team and public contributors jointly evaluating the process. Data were collected throughout the lifespan of the project by public contributors through photovoice, where they collected photos that represented their experiences of involvement. At the end of the evaluation meeting, 2 separate analyses were conducted. First, public contributors reflected on their experiences using a 4-dimensional framework to capture how strong their voice was, how many ways they had an opportunity to be involved, if their feedback was implemented, and if the discussion focused on their priorities. Second, they analyzed the collected photos by organizing them alongside their narratives, explaining their meanings and comparing how they experienced the involvement process. Results: Narratives from 8 photos illustrate public contributors' experience of involvement in these projects, presenting them in chronological order, showing how their perspectives evolved from not knowing what form the project would take, through understanding foundations and building confidence through being satisfied with the successful projects. Results from the 4-dimensional framework showed that public contributors felt that their voices were strong, and the research and clinical team mostly implemented suggested changes. The discussion focused on topics and issues that were relevant to public contributors. However, how public contributors were involved depended mainly on the research team's decision, and they would have preferred more opportunities. Conclusions: This study has shown that public contributors can be meaningfully involved throughout the lifespan of cancer research projects. The evaluation demonstrated that establishing a strong relationship and trust between researchers and public contributors helps to ensure that the public contributors' voice is meaningful and makes a difference in the projects. However, it also identified improvements for future public involvement. Researchers should involve public contributors as early as the funding application stage to offer more opportunities to shape research and thus have diverse involvement opportunities at each stage of the research process.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,777
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0160,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,005
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,917
Tête enseignante GPT0,768
Écart entre enseignants0,149 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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