PAeDS-MoRe: A framework for the development and review of research assent protocols involving children and adolescents
Bibliographic record
Abstract
We systematically reviewed contemporary literature to create an evidence-informed framework for research studies involving children and adolescents who can assent to participate. We searched seven citation indices to locate peer-reviewed research published in English language journals between 2000 and 2012. After screening 1,231 titles and abstracts for relevance, we assessed levels of evidence, extracted information, and analysed content from 87 articles. Most articles narrowly focused on paediatric assent barriers and facilitators for decision-making about research participation. No articles provided a single, comprehensive ethical framework to guide the development and review of research assent protocols. We developed a 6-step framework that provides guidance to: prepare the child for the assent process; assess the child’s readiness to engage in decision making; discuss the elements of informed consent to the greatest extent possible; seek an initial assent decision; monitor and affirm assent; and respect the child’s role as a research participant. The PAeDS-MoRe framework also supports the creation of process models that address the unique, developmental needs of paediatric sub-groups, and guides the operationalization of jurisdictional requirements for ethical research involving children who are unable to provide free, informed and ongoing consent.
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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 | 0.574 | 0.548 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.100 | 0.054 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.017 | 0.032 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".