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Enregistrement W4417019762 · doi:10.1186/s40900-025-00806-z

Patient engagement and shared decision-making in trial recruitment intervention studies: a systematic review

2025· review· en· W4417019762 sur OpenAlexafffund
Tamara L. Morgan, Natasha Hudek, Kelly Carroll, Mei-Lin Yee, Juliette Inglis, Dean Fergusson, Katie Gillies, Dawn P. Richards, Seana N. Semchishen, Justin Presseau, Jeremy Grimshaw, Ian D. Graham, Marc Rodger, Monica Taljaard, Susan Marlin, Charles Weijer, Graeme MacLennan

Notice bibliographique

RevueResearch Involvement and Engagement · 2025
Typereview
Langueen
DomaineHealth Professions
ThématiqueMental Health and Patient Involvement
Établissements canadiensWestern UniversityRobarts Clinical TrialsMcGill UniversityOttawa HospitalUniversity of Ottawa
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésPsychological interventionIntervention (counseling)Equity (law)Patient participationRandomized controlled trialClinical trialSystematic review

Résumé

récupéré en direct d'OpenAlex

Supporting participation decisions and experiences in clinical trials is a persistent challenge that could be improved by two areas: patient engagement (PE), which involves actively collaborating with patients to enhance research relevance and value, and shared decision-making (SDM), which involves helping individuals make evidence-informed, values-based decisions about participation. The extent to which PE and SDM have informed trial recruitment interventions has not been synthesized. We aimed to explore (1) how PE informed recruitment interventions, both in general and among equity-deserving populations, and whether demographic differences existed between studies using and not using PE, and (2) how SDM has informed recruitment interventions, both in general and among equity-deserving populations. We identified randomized and quasi-randomized recruitment intervention studies from a prior Cochrane review and the Online Resource for Research in Clinical triAls database. We assessed recruitment interventions for reporting of PE and coded the level at which PE occurred (‘substantive engagement’, ‘limited engagement’, ‘both’, ‘unclear’, or ‘no engagement’) and the areas in which PE occurred (development of the research question, intervention design, selecting outcomes, dissemination/implementation, or ‘other’). We coded SDM across six domains: providing information about options, probabilities, clarifying outcomes, guidance in deliberation, using evidence, and disclosure and transparency. Of the 122 recruitment intervention studies included, 37 (30.3%) reported PE, although limited engagement was most common (n = 22; 59.5%). PE was most often used in designing the recruitment intervention (n = 32; 86.5%) followed by ‘other’ (n = 11; 29.7%; e.g., PE supporting participant recruitment efforts), developing the research question (n = 2; 5.4%), selecting outcomes (n = 3; 8.1%), and dissemination/implementation (n = 3; 8.1%). SDM was occasionally reported (n = 25; 20.5%), most commonly as ‘providing information about options’ (n = 11; 9.0%). Equity-deserving populations were the focus of 24 studies (19.7%); 11 of these also used PE (9.0%). Efforts to improve trial participation have not been informed by literature around patient lived experiences. Recruitment interventions infrequently reported any PE and occasionally mentioned SDM. When PE was mentioned, it was usually limited. These results hold among studies involving equity-deserving populations. Greater consideration of PE and SDM could enhance trial recruitment, research impact, trial participation experiences, and equity in trial recruitment. Getting people to participate in clinical trials is challenging. Two approaches that can help are working closely with patients to ensure the research is important to them (called patient engagement, or PE) and helping them understand their options to make informed choices about whether to participate (called shared decision-making, or SDM). We wanted to find out how PE and SDM are used when recruiting people for trials in general and when recruiting populations that are often left out of trials. We also wanted to find out the differences between studies that use PE and those that do not. We reviewed studies about how people are recruited into trials. We checked whether these studies used PE, how engaged patients were (ranging from very engaged to not engaged at all), and where they were engaged, such as helping to choose the research question or sharing results. We also reviewed how SDM was used, such as providing information about options or using evidence to help people decide. Of 122 studies, only 37 mentioned PE, mostly at low levels of engagement. PE was mostly used to help design the recruitment strategy. Only 25 studies mentioned SDM, mainly by providing people with information. Only 24 studies focused on groups that are often overlooked, and only 11 of these used PE. Most efforts to ask people to join trials have not reflected patients’ real-life experiences. Using PE and SDM more effectively could help more people participate and lead to fairer recruitment.

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)0,043
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,296
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0430,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0010,004
Intégrité de la recherche0,0000,003
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 Opus0,814
Tête enseignante GPT0,648
Écart entre enseignants0,166 · 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'étudeRevue systématique
Domainenon disponible
GenreSynthèse

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'admission2
Résumé présentoui

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