Co‐designing clinical trials alongside youth with chronic pain
Bibliographic record
Abstract
Youth have a right to participate in research that will inform the care that they receive. Engagement with children and young people has been shown to improve rates of enrollment and retention in clinical trials as well as reduce research waste. The aim of the study is to gain practical insight on the design of trials specifically on (1) recruitment and retention preferences, (2) potential barriers to research, and (3) study design optimization. Based on this youth engagement, we will co-design two clinical trials in headaches with youth. Two recruitment strategies were used to recruit 16 youth from across Canada (aged 15-18 years) from an existing youth group, the KidsCan Young Persons' Research Advisory Group (YPRAG) and a new youth group in collaboration with Solutions for Kids in Pain (SKIP). Four virtual, semi-structured discussion groups were held between April and December 2020, which included pre-circulated materials and utilized two distinct upcoming planned trials as examples for specific methods feedback. Individual engagement evaluations were completed following the final group session using the Public and Patient Engagement Evaluation Tool. Descriptive results were shared with participants prior to publication to ensure appropriate interpretation. The discussion was centred around three themes: recruitment and retention preferences, potential barriers to participation, and study design optimization. Youth indicated that they would prefer to be contacted for a potential study directly by their physician (not over social media), that they would like to develop rapport with study staff, and that one of the barriers to participation is the time commitment. The youth also provided feedback on the design of the clinical trial including outcome measurement tools, data collection, and engagement methods. Feedback on the virtual format of the engagement events indicated that participants appreciated the ease of the online discussion and that the open-ended discussion allowed for easy exchange of ideas. They felt that despite a gender imbalance (towards females) it was an overall inclusive environment. All participants reported believing that their engagement will make a difference to the work of the research team in designing the clinical trials. Perspectives from a diverse group of youth meaningfully improved the design and conduct of two clinical trials for headaches in children. This study provides a framework for future researchers to engage youth in the co-design of clinical trials using online engagement sessions.
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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 | 0.081 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".