Strengths, challenges, and strategies for implementing pragmatic multicenter randomized controlled trials (RCTs): example of the Personalized Citizen Assistance for Social Participation (APIC) trial
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.148 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".