Indigenous communities and the mental health impacts of land dispossession related to industrial resource development: a systematic review
Bibliographic record
Abstract
Globally, many resource extraction projects such as mines and hydroelectric dams are developed on the territories of Indigenous Peoples. Recognising land as a determinant of Indigenous Peoples' health, our objective is to synthesise evidence about the mental health impacts on Indigenous communities who experience land dispossession due to industrial resource development (mining, hydroelectric, petroleum, and agricultural). We systematically reviewed studies that focused on Indigenous land dispossession in Australia, Aotearoa (New Zealand), North and South America, and the Circumpolar North. We searched Scopus, Medline, Embase, PsycINFO, and Global Health on OVID for peer-reviewed articles published in English from database inception to Dec 31, 2020. We also searched for books, research reports, and scholarly journals specialising in Indigenous health or Indigenous research. We included documents that reported on primary research, focused on Indigenous Peoples in settler colonial states, and reported on mental health and industrial resource development. Of the 29 included studies, 13 were related to hydroelectric dams, 11 to petroleum developments, nine to mining, and two to agriculture. Land dispossession due to industrial resource development had predominantly negative mental health impacts on Indigenous communities. The impacts were consequences of colonial relations that threatened Indigenous identities, resources, languages, traditions, spirituality, and ways of life. Health impact assessment processes in industrial resource development must expressly consider risks and potential impacts on mental health and respect Indigenous rights by making knowledge about mental health risks a central component to decisions about free, prior, and informed consent.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".