Access to land and nature as health determinants: a qualitative analysis exploring meaningful human-nature relationships among Indigenous youth in central Canada
Bibliographic record
Abstract
BACKGROUND: Human relationships with and connections to nature and the "land" are a commonly accepted Social Determinant of Health. Greater knowledge about these relationships can inform public health policies and interventions focused on health equity among Indigenous populations. Two research questions were explored: (1) what are the experiences of meaningful human-nature relationships among Indigenous youth within central Canada; and (2) how do these relationships function as a determinant of health and wellness within their lives. METHODS: Drawing from three community-based participatory research (CBPR) projects within two urban centers in Saskatchewan and Manitoba, the integrated qualitative findings presented here involved 92 interviews with 52 Indigenous youth that occurred over a period of nine years (2014-2023). Informed by "two-eyed seeing," this analysis combined Indigenous Methodologies and a Constructivist Grounded Theory approach. RESULTS: Our integrative analysis revealed three cross-cutting themes about meaningful human-nature relationships: (1) promoting cultural belonging and positive identity; (2) connecting to community and family; and (3) supporting spiritual health and relationships. The experiences of young people also emphasized barriers to land and nature access within their local environments. DISCUSSION: Policies, practices, and interventions aimed at strengthening urban Indigenous young peoples' relationships to and connections with nature and the land can have a positive impact on their health and wellness. Public Health systems and healthcare providers can learn about leveraging the health benefits of human-nature relationships at individual and community levels, and this is particularly vital for those working to advance health equity among Indigenous populations.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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, 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".