Indigenous youth engagement in research: a scoping review of community-based participatory action research (CBPAR) in Canada and the USA
Bibliographic record
Abstract
Community-based participatory action research (CBPAR) is founded on meaningful collaboration with communities in the design and execution of research. CBPAR is gaining traction as an ethical and effective approach to research with underserved populations, including Indigenous youth. This scoping review examined 134 CBPAR studies involving Indigenous youth in Canada and the United States, to identify when, how, and to what extent Indigenous youth were engaged in research. Youth engagement was analysed on a continuum defined by the extent of decision-making throughout research. Indigenous youth participated in research decision-making in 62% of studies examined, with wide variation in extent and style of engagement. Youth wisdom and voices were often underutilized or undescribed in research decisions. However, many cases demonstrate that Indigenous youth can and do contribute meaningfully to research with and about their peers. Findings support a need for transparent reporting on how youth engagement is enacted in CBPAR.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: yes · About a Canadian topic: yes | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: yes | Systematic review | high |
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 | 0.036 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.020 | 0.034 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".