Benefits, barriers and recommendations for youth engagement in health research: combining evidence-based and youth perspectives
Bibliographic record
Abstract
BACKGROUND: Youth engagement refers to the collaboration between researchers and youth to produce research. Youth engagement in health research has been shown to inform effective interventions aimed at improving health outcomes. However, limited evidence has identified promising practices to meaningfully engage youth. This synthesis aims to describe youth engagement approaches, frameworks, and barriers, as well as provide both evidence-based and youth-generated recommendations for meaningful engagement. MAIN BODY: This review occurred in two stages: 1) a narrative review of existing literature on youth engagement and 2) a Youth Advisory Council (YAC) to review and supplement findings with their perspectives, experiences, and recommendations. The terms 'youth engagement' and 'health research' were searched in Google Scholar, PubMed, Web of Science, Scopus, and PsycINFO. Articles and non-peer reviewed research works related to youth engagement in health research were included, reviewed, and summarized. The YAC met with research team members and in separate youth-only forums to complement the narrative review with their perspectives. Types of youth engagement include participation as research participants, advisors, partners, and co-investigators. Barriers to youth engagement were organized into youth- (e.g., time commitments), researcher- (e.g., attitudes towards youth engagement), organizational- (e.g., inadequate infrastructure to support youth engagement), and system-level (e.g., systemic discrimination and exclusion from research). To enhance youth engagement, recommendations focus on preparing and supporting youth by offering flexible communication approaches, mentorship opportunities, diverse and inclusive recruitment, and ensuring youth understand the commitment and benefits involved. CONCLUSIONS: To harness the potential of youth engagement, researchers need to establish an inclusive and enabling environment that fosters collaboration, trust, and valuable contributions from youth. Future research endeavors should prioritize investigating the dynamics of power-sharing between researchers and youth, assessing the impact of youth engagement on young participants, and youth-specific evaluation frameworks.
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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 | 0.331 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.007 | 0.028 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".