Land and nature as sources of health and resilience among Indigenous youth in an urban Canadian context: a photovoice exploration
Bibliographic record
Abstract
BACKGROUND: Population and environmental health research illustrate a positive relationship between access to greenspace or natural environments and peoples' perceived health, mental health, resilience, and overall well-being. This relationship is also particularly strong among Canadian Indigenous populations and social determinants of health research where notions of land, health, and nature can involve broader spiritual and cultural meanings. Among Indigenous youth health and resilience scholarship, however, research tends to conceptualize land and nature as rural phenomena without any serious consideration on their impacts within urban cityscapes. This study contributes to current literature by exploring Indigenous youths' meaning-making processes and engagements with land and nature in an urban Canadian context. METHODS: Through photovoice and modified Grounded Theory methodology, this study explored urban Indigenous youth perspectives about health and resilience within an inner-city Canadian context. Over the course of one year, thirty-eight in-depth interviews were conducted with Indigenous (Plains Cree First Nations and Métis) youth along with photovoice arts-based and talking circle methodologies that occurred once per season. The research approach was also informed by Etuaptmumk or a "two-eyed seeing" framework where Indigenous and Western "ways of knowing" (worldviews) can work alongside one another. RESULTS: Our strength-based analyses illustrated that engagement with and a connection to nature, either by way of being present in nature and viewing nature in their local urban context, was a central aspect of the young peoples' photos and their stories about those photos. This article focuses on three of the main themes that emerged from the youth photos and follow-up interviews: (1) nature as a calming place; (2) building metaphors of resilience; and (3) providing a sense of hope. These local processes were shown to help youth cope with stress, anger, fear, and other general difficult situations they may encounter and navigate on a day-to-day basis. CONCLUSIONS: This study contributes to the literature exploring Indigenous youths' meaning-making process and engagements with land and nature in an urban context, and highlights the need for public health and municipal agencies to consider developing more culturally safe and meaningful natural environments that can support the health, resilience, and well-being of Indigenous youth within inner-city contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".