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Enregistrement W4414518039 · doi:10.1101/2025.09.19.25336090

Identifying Challenges and Enablers to Engaging Patients in Preclinical Laboratory Research

2025· preprint· en· W4414518039 sur OpenAlexafffundabout
Madison Foster, Dean Fergusson, Emily Thompson, Victoria Hunniford, Talston Scott, Stephen R. Daniels, Dawn P. Richards, Pat Messner, Kathryn Hendrick, Patrick Sullivan, Asher A. Mendelson, Kimberly F. Macala, Kirsten M. Fiest, Angela M. Crawley, Bernard Thébaud, Stuart G. Nicholls, Cheryle A. Séguin, Grace Fox, Justin Presseau, Manoj M. Lalu

Notice bibliographique

RevuemedRxiv · 2025
Typepreprint
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensWestern UniversityInstitute of Infection and ImmunityUniversity of CalgaryAlberta Health ServicesUniversity of OttawaRoyal Alexandra HospitalUniversity of AlbertaOttawa HospitalUniversity of ManitobaCARE Canada
Organismes subventionnairesStem Cell NetworkManitoba Medical Service FoundationCanadian Institutes of Health ResearchUniversity of Ottawa
Mots-clésThematic analysisJargonPreclinical researchStakeholder engagementQualitative researchPublic engagementValue (mathematics)

Résumé

récupéré en direct d'OpenAlex

Abstract Background Patient engagement in research enriches study design, conduct, and dissemination by integrating lived experiences of patients into the research process. Although patient engagement is increasingly common in clinical research settings, it remains rare in preclinical (i.e. laboratory based) research. To explore this gap, we conducted an interview study to understand how early adopters have implemented patient engagement in this area, focusing on the challenges and benefits of their approach. Methods We conducted semi-structured interviews of patients (n=15) and researchers (n=14) with previous preclinical patient engagement experience. Interviews were transcribed and conducted using an inductive, thematic content analysis, which allowed for bottom-up analysis of interview data. Our team inclusive of patients, clinical, preclinical and patient engagement researchers identified, reviewed, and refined emerging themes. Results We identified five themes. Researchers and patients highlighted the necessity to adopt a thoughtful and tailored approach for each preclinical engagement initiative (Theme 1). Clear communication was deemed critical, suggesting the need for a clear and shared vocabulary without technical jargon (Theme 2). This includes taking time to cultivate personal relationships, co-develop engagement activities to meet patient and researcher preferences and needs. In addition, v aried goals for engagement in preclinical research between researchers and patients was underscored (Theme 3), indicating the need to discuss aims and motivations early and often as well as to co-develop mutually beneficial strategies. Researchers and patients also discussed how they require a better understanding of the value of preclinical patient engagement (Theme 4). This could be fostered through education and illustrative case examples. Finally, a shift in research culture was deemed necessary (Theme 5), and called for stronger institutional support, efficient channels to connect preclinical researchers and patients, as well as initiatives that recognize and champion preclinical patient engagement. Conclusion Our study identified five common themes in preclinical patient engagement which can help the research community facilitate meaningful engagement of patients in preclinical laboratory research. Funding This work was supported by a Canadian Stem Cell Network Ethical, Legal and Social Implications Operating Grant. Patient engagement was supported by a Canadian Institutes of Health Research (CIHR) Strategy for Patient Oriented Research Catalyst Grant: Patient-Oriented Research. MML is supported by The Ottawa Hospital Anesthesia Alternate Funds Association, a University of Ottawa Junior Research Chair in Innovative Translational Research as well as the Canadian Anesthesia Research Foundation funded Canadian Anesthesiologists’ Society Career Scientist Award. AAM is supported by the Manitoba Medical Services Foundation Dr. F. W. DuVal and John Henson Clinical Research Professorship. Plain English Summary Engaging patients as partners in clinical research, known as patient engagement, is a growing practice that has numerous benefits. However, uptake in preclinical laboratory research (e.g. cell and animal studies) has been limited. Nevertheless, incorporating patients as active collaborators at this discovery stage of biomedical research may be beneficial. To better understand how patient engagement fits into preclinical research, we conducted interviews with patient partners and preclinical researchers who have been early adopters of this practice. Five key themes emerged. First, both groups emphasized the need for adopting a thoughtful and tailored approach since preclinical research is not typically patient facing. Second, shared vocabulary was important to facilitate communication. Third, setting clear expectations and outlining varied goals for engagement was considered critical. Fourth, understanding the value of preclinical research helped ground engagement efforts. Finally, interviewees felt a cultural shift is needed for this practice to be accepted more widely. These themes are important factors to consider when engaging patients in preclinical laboratory research; they may be used to inform and support future preclinical patient engagement 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,191
score de la tête « metaresearch » (Gemma)0,230
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,191
Score d'incertitude au seuil0,998

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1910,230
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0080,015
Communication savante0,0180,012
Science ouverte0,0050,021
Intégrité de la recherche0,0050,009
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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 Opus0,666
Tête enseignante GPT0,579
Écart entre enseignants0,086 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeQualitatif
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

Citations0
Publié2025
Routes d'admission3
Résumé présentoui

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