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Enregistrement W4416791451 · doi:10.1186/s40900-025-00818-9

Identifying challenges and enablers to engaging patients in preclinical laboratory research: an interview study

2025· article· en· W4416791451 sur OpenAlexafffund
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

RevueResearch Involvement and Engagement · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensWestern UniversityInstitute of Infection and ImmunityUniversity of CalgaryAlberta Health ServicesUniversity of AlbertaOttawa HospitalUniversity of ManitobaCARE CanadaGlycemic Index LaboratoriesRoyal Alexandra HospitalUniversity of Ottawa
Organismes subventionnairesOttawa Hospital Anesthesia Alternate Funds AssociationStem Cell NetworkManitoba Medical Service FoundationCanadian Institutes of Health ResearchCanadian Anesthesia Research Foundation
Mots-clésThematic analysisTheme (computing)Qualitative researchPatient experienceInterpretation (philosophy)Vocabulary

Résumé

récupéré en direct d'OpenAlex

Patient engagement in research enriches study design, conduct, and dissemination by integrating lived experiences of patients into the research process. Although patient engagement is becoming more popular in clinical research settings, it remains comparatively rare in preclinical (i.e. laboratory based) research. To explore this gap, we conducted an interview study to understand how researchers and patients have implemented patient engagement in this area, focusing on the challenges and benefits of their approach. We conducted semi-structured interviews of patients (n = 15) and researchers (n = 14) with previous preclinical patient engagement experience. Interviews were transcribed and reviewed using an inductive, thematic content analysis, which allowed for bottom-up analysis of interview data. Our team identified, reviewed and refined emerging themes. Our team members include preclinical, clinical and patient engagement researchers and patient partners, which allowed for various perspectives to contribute to the final interpretation of the findings and drafting of the manuscript. We identified five themes. Theme 1: Researchers and patients highlighted the necessity to adopt a thoughtful and tailored approach for each preclinical engagement initiative. This includes taking time to cultivate personal relationships and co-developing engagement activities to meet patient and researcher preferences and needs. Theme 2: Clear communication was deemed critical, suggesting the need for a clear and shared vocabulary without technical jargon. Theme 3: Varied goals for engagement in preclinical research between researchers and patients were underscored, indicating the need to discuss aims and motivations early and often as well as to co-develop mutually beneficial strategies. Theme 4: Researchers and patients also discussed how their communities require a better understanding of the value of preclinical patient engagement. This could be fostered through education and illustrative case examples. Theme 5: Finally, a shift in research culture was deemed necessary 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. 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. 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 implemented 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)0,047
score de la tête « metaresearch » (Gemma)0,066
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Méthodes · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,953
Score d'incertitude au seuil0,246

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0470,066
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0100,009
Communication savante0,0060,006
Science ouverte0,0020,008
Intégrité de la recherche0,0040,007
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

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 Opus0,878
Tête enseignante GPT0,644
Écart entre enseignants0,234 · 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
DomaineMéthodes
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

Citations2
Publié2025
Routes d'admission2
Résumé présentoui

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