Cree Youth Engagement in Health Planning
Bibliographic record
Abstract
Indigenous communities experience a greater burden of ill health than all other communities in Canada. Across the (Indigenous Region), all nine (Name) communities experience similar health challenges. In 2014, the (REGIONAL_BOARD) supported an initiative to stimulate local community prioritization for health change. While many challenges identified were specific to youth (10-29 years of age), youth’s perspectives in these reports to date have been limited. We sought to understand how (Indigenous) youth perceived youth health and their engagement in health and health planning across (Region). As part of a (REGIONAL_BOARD-University) partnership, this qualitative descriptive study adopted a community-based participatory research approach. Ten (Indigenous) youth participated in two focus groups, and five (Indigenous) youth coordinators participated in key informant interviews. Thematic analysis was conducted and inductive codes were grouped into themes. (Indigenous) participants characterized youth engagement into the following levels: participation in community and recreational activities; membership in youth councils at the local and regional levels; and, in decision-making as planners of health-related initiatives. (Indigenous) youth recommended greater use of social media, youth assemblies, and youth planners to strengthen their engagement and youth health in the region. Our findings revealed an interconnectedness between youth health and youth engagement; (Indigenous) youth described how they need to be engaged to be healthy, and need to be healthy to be engaged. (Indigenous) participants contributed novel and practical insights to engage Indigenous youth in health planning across Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".