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Enregistrement W4413101323 · doi:10.1111/hex.70365

Community Engagement in Long Covid Research: Process, Evaluation and Recommendations From the Long COVID and Episodic Disability Study

2025· article· en· W4413101323 sur OpenAlexafffundabout
Margaret O’Hara, Kiera McDuff, Hannah Wei, Lisa McCorkell, Catherine Thomson, Mary Kelly, Susie Goulding, Imelda O’Donovan, Sarah O’Connell, Ruth Stokes, Nisa Malli, Natalie St. Clair‐Sullivan, Soo Chan Carusone, Angela M. Cheung, Kristine M. Erlandson, Ciarán Bannan, Liam Townsend, Colm Bergin, Jaime H. Vera, Richard Harding, Lisa Avery, Darren A. Brown, Kelly K. O’Brien

Notice bibliographique

RevueHealth Expectations · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueLong-Term Effects of COVID-19
Établissements canadiensToronto Rehabilitation InstitutePrincess Margaret Cancer CentreUniversity Health NetworkUniversity of TorontoCanadian Patient Safety InstituteHamilton Health SciencesTD Bank GroupPublic Health OntarioMcMaster University Medical Centre
Organismes subventionnairesCanadian Institutes of Health ResearchPittsburgh Liver Research Center, University of PittsburghCanada Research Chairs
Mots-clésBalanced scorecardPsychologyCoronavirus disease 2019 (COVID-19)Medical educationComputer-assisted web interviewingProcess (computing)Community engagementMedicinePublic relationsPolitical scienceProcess managementBusinessDisease

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Long Covid and other infection-associated chronic condition communities have been integral in advocating for patient engagement in all stages of research, from design and conduct, and implementation, through to interpretation and knowledge translation; nevertheless, the process varies across research teams. In this paper, we (1) describe our process undertaking a community-engaged Long Covid research study; (2) evaluate our community-engaged approach, highlighting strengths and limitations with our process; and (3) identify recommendations for engaging in community-engaged patient-oriented research in Long Covid. METHODS: Guided by the 4PI (Principles; Purpose; Presence; Process; Impact) Framework and Patient-Led Research Scorecards, we describe our community-engaged approach within the Long COVID and Episodic Disability Study, followed by an evaluation of our community engagement using a multistage consultation with members of the Long COVID and Episodic Disability Study team. We conducted an online group-based discussion among persons with lived experiences and administered a web-based Scorecard questionnaire rating the collaboration as it relates to four domains of patient burden, governance, integration into the research process, and organisation readiness to all members of the team, to assess strengths and limitations of our approach. Scores ranged from -2 (non-collaboration) to +2 (ideal collaboration). RESULTS: Ten team members, five of whom were persons with lived experiences, completed the Scorecard questionnaire. Median Scorecard scores ranged from +1 to +2 for all domains. Five team members with lived experiences, representing four community support groups and organisations that participated in the community-engagement discussion. We describe the practices and principles that enabled meaningful community engagement, with the strengths and limitations of our approach embedded throughout. CONCLUSION: Our community-engaged approach to the Long COVID and Episodic Disability Study enhanced the quality and relevance of the study to the community while highlighting areas to heighten meaningful engagement throughout. This study builds on foundational community-based research principles of patient-oriented research. Recommendations derived from our experiences may be used by other research teams conducting community-engaged patient-oriented research. PATIENT OR PUBLIC CONTRIBUTION: The Long COVID and Episodic Disability Study is a community-engaged research study involving 25 members, including 12 persons living with long Covid, 13 researchers and 5 clinicians (categories are not mutually exclusive), referred to as the Full Team. Persons with lived experiences possessed a range of professional and personal experiences spanning research, clinical, policy and private sector/business contexts; team members wore multiple hats and perspectives which collectively strengthened the diversity of expertise, perspectives and insights to the team and process. Engagement of people with lived experiences with Long Covid ensured that the study was fully co-created with people living with Long Covid. During the development of the study proposal, community partners from organisations in Canada, Ireland, the United Kingdom and the United States, who were linked to larger networks of people living with Long Covid, were purposefully invited to join the study team. Several Long Covid community networks and organisations, represented by persons living with Long Covid, were involved in all stages of the research, including: COVID Long-Haulers Support Group Canada (S.G.); Long COVID Advocacy Ireland (I.O., S.O. and R.S.); Long COVID Ireland (N.R. and R.S.); Long COVID Physio (D.A.B. and C.T.); Long Covid Support UK (M.O.H.); and Patient-Led Research Collaborative (L.M., N.M. and H.W.). These representatives along with the Co-PIs (K.K.O. and D.A.B.) and co-ordinator (K.M.) comprised the Core Long COVID and Episodic Disability Community Collaborator Team (Core Team). Team members with lived experiences were provided yearly remuneration for their time and expertise dedicated to the study, either as an individual, or to the community organisation which they represented on the study according to their preference.

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)0,007
score de la tête « metaresearch » (Gemma)0,010
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,147
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,198
Tête enseignante GPT0,520
Écart entre enseignants0,322 · 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'étudeObservationnel
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

Citations3
Publié2025
Routes d'admission3
Résumé présentoui

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