Research‐ and health‐related youth advisory groups in Canada: An environmental scan with stakeholder interviews
Bibliographic record
Abstract
BACKGROUND: Engaging youth throughout the research process improves research quality and outcomes. Youth advisory groups provide one way for youth to express their opinions on relevant issues. OBJECTIVE: This study aimed to identify research- and health-related youth advisory groups ('groups') in Canada and understand the best practices of these groups. METHODS: Google searches and supplementary methods were used to identify relevant groups in Canada. Group information was extracted from websites or through interviews with key informants. RESULTS: We identified 40 groups. Groups were commonly part of a hospital/healthcare facility, nonprofit/health organization or research group. The majority focused on a specific content area, most commonly, mental health. Over half the groups advised on health services. Members' ages ranged from 9 to 35 years. The number of members ranged from 5 to 130. Interviews (n = 12) identified seven categories relating to group practices: (a) group purpose/objectives, (b) group development, (c) group operations, (d) group structure, (e) adult involvement, (f) membership and recruitment and (g) group access. Challenges and facilitators to the success of groups were described within the following themes: (a) retaining engagement, (b) creating a safe environment and (c) putting youth in positions of influence. Advice and recommendations were provided regarding the development of a new group. CONCLUSION: This study provides a comprehensive overview of research- and health-related youth advisory groups in Canada. This information can be used to identify groups that stakeholders could access as well as inform the development of a new group. PATIENT OR PUBLIC CONTRIBUTION: Youth advisory group representatives were interviewed as part of the study.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".