Youth participatory research evidence to inform health policy: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Young people's participation in health research produces knowledge that is indispensable for creating appropriate and effective policies. However, how best to disseminate youth participatory research evidence to impact health policy is not known. Therefore, the objectives of this systematic review are to describe the evidence produced through youth participatory research, including the strategies used to disseminate youth participatory research evidence to health policymakers. These are necessary to improve policymakers' use of youth participatory research evidence and, thereby, make programmes more impactful for young people. METHODS AND ANALYSIS: The meta-narrative methodology will guide the systematic review to highlight the contrasting and complementary evidence on the use of engaging youth in research to affect health policymaking. Relevant studies will be identified by searching electronic databases, including but not limited to EBSCO, PROQUEST, OVID Medline, Sociological Abstracts and Google Scholar from inception to December 2020. The methodological quality of included quantitative, qualitative and mixed-methods research studies will be assessed using valid appraisal tools. The meta-narrative approach to analysis will include identifying meta-narratives of how youth participation informed the health research findings. ETHICS AND DISSEMINATION: An advisory group of young people will advise on the study and dissemination of the findings. As part of our plan for active dissemination, we will produce a policy brief that builds the rationale for using research with and by youth as part of an evidence base necessary for achieving youth health outcomes.
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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 | 0.182 | 0.146 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.077 | 0.017 |
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".