Guidance on development and operation of Young Persons’ Advisory Groups
Bibliographic record
Abstract
BACKGROUND: Engaging patients and the public as collaborators in research is increasingly recognised as important as such partnerships can help improve research relevance and acceptability. Young Persons' Advisory Groups (YPAGs) provide a forum for clinical researchers and triallists to engage with children and young people on issues relevant to the design, conduct and translation of paediatric clinical trials. Until fairly recently, there was very little information available to guide the successful development and operation of YPAGs. OBJECTIVE: To develop an evidence-based tool to guide clinical researchers and triallists in the establishment and operation of a YPAG. METHODS: An online needs assessment survey was conducted using SurveyMonkey with 60 known paediatric drug researchers to identify knowledge gaps around YPAG engagement, development and operation. Semistructured interviews with founders and coordinators of five well-established existing YPAGs and a review of the literature were performed to identify best-practice processes for starting up and operating YPAG. RESULTS: The majority of 12 survey respondents (20%) from 12 different centres indicated that while they felt YPAGs could benefit their research, guidance on how to develop and operate a YPAG was needed. Most preferred a web-based guidance tool. Ten core steps in starting up and operating a YPAG were identified and developed into an online YPAG guidance tool, now freely accessible for use by paediatric clinical researchers worldwide. Plans to evaluate the impact are in place. CONCLUSIONS: This novel tool, developed with an internationally based group of public involvement leads working across paediatric clinical research areas, provides harmonised guidance for researchers seeking to develop and operate YPAGs to help improve the quality and impact of paediatric clinical research studies.
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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 | 0.173 | 0.293 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.052 | 0.051 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".