Connection to the land as a youth-identified social determinant of Indigenous Peoples’ health
Bibliographic record
Abstract
BACKGROUND: Social determinants of Indigenous health are known to include structural determinants such as history, political climate, and social contexts. Relationships, interconnectivity, and community are fundamental to these determinants. Understanding these determinants from the perspective of Indigenous youth is vital to identifying means of alleviating future inequities. METHODS: In 2016, fifteen Yellowknives Dene First Nation (YKDFN) youth in the Canadian Northwest Territories participated in the 'On-the-Land Health Leadership Camp'. Using a strength- and community-based participatory approach through an Indigenous research lens, the YKDFN Wellness Division and university researchers crafted the workshop to provide opportunities for youth to practice cultural skills, and to capture the youth's perspectives of health and health agency. Perspectives of a healthy community, health issues, and health priorities were collected from youth through sharing circles, PhotoVoice, mural art, and surveys. RESULTS: The overall emerging theme was that a connection to the land is an imperative determinant of YKDFN health. Youth identified the importance of a relationship to land including practicing cultural skills, Elders passing on traditional knowledge, and surviving off the land. The youth framed future health research to include roles for youth and an on-the-land component that builds YKDFN culture, community relations, and traditional knowledge transfer. Youth felt that a symbiotic relationship between land, environment, and people is fundamental to building a healthy community. CONCLUSION: Our research confirmed there is a direct and critical relationship between structural context and determinants of Indigenous Peoples' health, and that this should be incorporated into health research and interventions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".