Recruitment and Retention Strategies for Randomized Clinical Trials Involving Family Members of ICU Patients
Bibliographic record
Abstract
OBJECTIVES: To review recruitment and retention strategies of randomized family-centered interventional studies in adult ICUs. DATA SOURCES: The MEDLINE, Embase, PsycINFO, CINAHL, and the Cochrane Library database from inception to February 2023. STUDY SELECTION: Randomized controlled trials with family-centered interventions in the ICU setting that reported at least one family-centered outcome that were included in our previously published systematic review. DATA EXTRACTION: For recruitment: Number of family members approached and enrolled, type of approach, location, time of day approached, whether medical team approached first, compensation offered, and type of consent. For retention: Number of family members enrolled and completed initial follow-up visit, mode of follow-up, location of follow-up visit, data collection method, timing of follow-up visits, number of follow-up visits, and compensation offered. Recruitment (participants approached/enrolled) and retention (participants enrolled/completed initial follow-up) percentage were calculated. DATA SYNTHESIS: There were 51 studies in the analysis. The mean recruitment percentage was 49.3% ± 24.3%. There were no differences in recruitment percentage by study country, ICU type, recruitment approach, or whether the medical team approached the family member first (all p > 0.05). The mean retention percentage for the initial follow-up visit was 81.6% ± 18.0%. There were no differences in retention percentage by mode of participant contact, data collection type, or follow-up location (all p > 0.05). Minimal data were available to determine the impact of time of day approached and compensation on recruitment and retention outcomes. CONCLUSIONS: About half of family members of ICU patients approached participated in trials and more than eight in ten completed the initial follow-up visit. We did not identify specific factors that impacted family recruitment or retention. There is a strong need for further studies to characterize optimal strategies to ensure family participation in clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.353 | 0.620 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.018 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".