Strengths, challenges, and strategies for implementing pragmatic multicenter randomized controlled trials (RCTs): example of the Personalized Citizen Assistance for Social Participation (APIC) trial
Notice bibliographique
Résumé
BACKGROUND: Randomized controlled trials (RCTs) are rigorous scientific research designs for evaluating intervention effectiveness. However, implementing RCTs in a real-world context is challenging. To develop strategies to improve its application, it is essential to understand the strengths and challenges of this design. This study thus aimed to explore the strengths, challenges, and strategies for improving the implementation of a pragmatic multicenter, prospective, two-arm RCT evaluating the effects of the Personalized Citizen Assistance for Social Participation (Accompagnement-citoyen Personnalisé d'Intégration Communautaire: APIC; weekly 3-h personalized stimulation sessions given by a trained volunteer over a 12-month period) on older adults' health, social participation, and life satisfaction. METHODS: A multiple case study was conducted with 14 participants, comprising one research assistant, seven coordinators, and six managers of six community organizations serving older adults, who implemented the APIC in the context of a RCT. Between 2017 and 2023, qualitative data were extracted from 24 group meetings, seven semi-directed interviews, emails exchanged with the research team, and one follow-up document. RESULTS: Aged between 30 and 60 (median ± SIQR: 44.0 ± 6.3), most participants were women from organizations already offering social participation interventions for older adults and working with the public sector. Reported strengths of this RCT were its relevance in assessing an innovative intervention to support healthy aging, and the sharing of common goals, expertise, and strategies with community organizations. Challenges included difficulties recruiting older adults, resistance to potential control group assignments, design complexity, and efforts to mobilize and engage volunteers. The COVID-19 pandemic lockdown and health measures exacerbated challenges related to recruiting older adults and mobilizing volunteers and complicated delivery of the intervention. The strategies that mostly overcame difficulties in recruiting older adults were reducing sample size, simplifying recruitment procedures, emphasizing the health follow-up, extending partnerships, and recognizing and supporting volunteers better. Because of the lockdown and physical distancing measures, the intervention was also adapted for remote delivery, including via telephone or videoconferencing. CONCLUSION: Knowledge of the strengths and challenges of pragmatic RCTs can contribute to the development of strategies to facilitate implementation studies and better evaluate health and social participation interventions delivered under real-life conditions. TRIAL REGISTRATION: NCT03161860; Pre-results. Registered on May 22, 2017.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,148 | 0,153 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».