Parent engagement in co-design of clinical trials: the PARENT trial
Bibliographic record
Abstract
Evidence generated from partnering with parents to design and conduct research together may be used to refine, adjust, and modify future research approaches. This study aimed to describe the initial approaches to parent engagement in the design of the PARENT trial as well as understand parent perspectives on the acceptability and relevance of the PARENT trial and potential barriers and facilitators to participation.Parents participating in the TARGet Kids! cohort were invited to participate in a focus group, called the PARENT panel, to co-design the PARENT trial. This focus group was conducted to capture diverse individual and collective parents' experiences. Overall methodological approaches for the PARENT panel were informed by the CIHR Strategy for Patient Oriented Research (SPOR) guiding principles (mutual respect, co-building, inclusiveness, and support) for patient engagement in research, and facilitated through the Knowledge Translation Program in the Li Ka Shing Knowledge Institute at Unity Health Toronto. Using a Nominal Group Technique, the PARENT panel provided feedback on the feasibility, relevance, and acceptability of the proposed intervention. Findings from this work will be used to further refine, adjust, and modify the next iteration of the PARENT trial, which will also serve as an opportunity to discuss the efforts made by researchers to incorporate parent suggestions and what additional steps are required for improved patient engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.483 | 0.558 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".