Recruitment and retention challenges and strategies in randomized controlled trials of psychosocial interventions for children with cancer and their parents: a collective case study
Bibliographic record
Abstract
Abstract Objective In pediatric oncology there are few examples of successful recruitment and retention strategies in psychosocial care research. This study aims to summarize experiences, challenges, and strategies for conducting randomized controlled trials (RCTs) from psychosocial intervention studies among children with cancer and their parent(s). Methods We conducted a collective case study. To identify the cases, Pubmed and two trial registries were searched for ongoing and finished RCTs of psychosocial intervention studies for children with cancer and their parents. Online semi-structured expert interviews discussing recruitment and retention challenges and strategies were performed with principal investigators and research staff members of the intervention studies. Results Nine studies were identified. Investigators and staff from seven studies participated, highlighting challenges and strategies within three major themes: eligibility, enrollment and retention. Regarding eligibility, collaborating constructively with healthcare professionals and involving them before the start of the study were essential. Being flexible, training the research staff, enabling alignment with the participants' situation, and providing consistency in contact between the research staff member and the families were important strategies for optimizing enrollment and retention. All studies followed a stepped process in recruitment. Conclusion Although recruitment and retention in some selected studies were successful, there is a paucity of evidence on experienced recruitment and retention challenges in pediatric psychosocial research and best practices on optimizing them. The strategies outlined in this study can help researchers optimize their protocol and trial-implementation, and contribute to better psychosocial care for children with cancer and their parents. Trial registration: this study is not a clinical trial.
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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 | 0.787 | 0.778 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".