A systematic review of theories, models and frameworks used for youth engagement in health research
Bibliographic record
Abstract
BACKGROUND: Youth engagement in research, wherein youth are involved in the research beyond mere participation as human subjects, is growing and becoming more popular as an approach to research. However, systematic and deliberate theory-building has been limited. We conducted a systematic review to identify and synthesize theories, models and frameworks that have been applied in the engagement of youth in health research, including mental health. METHODS: Six academic databases (MEDLINE, PsycINFO, Embase, PubMed, Scopus, CINAHL) and the grey literature were searched for relevant studies. Citation tracking was conducted through ancestry and descendancy searches. The final search was completed on 7 February 2023. Findings were summarized in a narrative synthesis informed by principles of hermeneutic analysis and interpretation. Reporting of results is in accordance with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 Statement. RESULTS: Of the 1156 records identified, 16 papers were included, from which we extracted named theories (n = 6), implicit theories (n = 5) and models and frameworks (n = 20) used for youth engagement in health research. We identified theories that were explicitly stated and surfaced theories that were more implicitly suggested. Models and frameworks were organized into four categories based on their principal features: power-focused (n = 8), process-focused (n = 7), impact-focused (n = 3) and equity-focused (n = 2). Few frameworks (n = 5) were empirically tested in health-related research. CONCLUSIONS: The state of theoretical development in youth engagement in research is still evolving. In this systematic review, we identified theories, models and frameworks used for youth engagement in health research. Findings from this systematic review offer a range of resources to those who seek to develop and strengthen youth engagement in their own research. PATIENT OR PUBLIC CONTRIBUTION: Youth engaged as patients in the research were not involved in planning or conducting the systematic review. However, youth researchers in their early to mid-20s led the planning, implementation and interpretation of the review. As part of subsequent work, we formed a youth advisory board to develop a youth-led knowledge mobilization intended for an audience of youth with lived experience of being engaged as patients in research.
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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 | 0.099 | 0.280 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.042 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".