Indigenous mental health research in the context of climate change: methodological reflections on language and barriers to cultural practice
Bibliographic record
Abstract
Climate change creates unique forms of psychological distress for Indigenous Peoples whose identities and cultural practices are often intrinsically connected to ancestral lands, yet research on culturally appropriate methodologies for studying Indigenous mental health in the context of climate change remains limited. This perspective paper presents methodological reflections from Land Body Ecologies research collective, which collaborates with Ogiek (Kenya), Batwa (Uganda), Iruliga (India), Pgak'yau (Thailand) and Sámi (Sápmi) Indigenous Peoples to explore climate change-related mental health impacts through the lens of solastalgia. Through participatory dialogues conducted during in-person gatherings, team members reflected on three years of community-based participatory research and identified two critical methodological challenges underexplored in the existing literature: (1) language and concept translation difficulties, where terms such as 'mental health' and 'climate change' lack direct cultural equivalents and may carry stigmatising connotations and (2) barriers to cultural practices, where climate change and conservation-related legislation restricts Indigenous Peoples' access to ancestral lands and traditional practices essential for well-being. These challenges reflect deeper epistemological tensions between conventional research approaches and Indigenous holistic worldviews that understand land, body and ecosystems as interconnected. It concludes that meaningful mental health research with Indigenous Peoples demands active recognition of Indigenous cultural rights and self-determination, collaborative approaches that honour Indigenous knowledge systems and systemic changes that normalise Indigenous timelines, relationality and knowledge sovereignty within research institutions.
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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 | 0.271 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.025 | 0.070 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.006 | 0.013 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".