Impact on mental health and wellbeing in Indigenous communities due to land loss resulting from industrial resource development: protocol for a systematic review
Bibliographic record
Abstract
BACKGROUND: Indigenous Peoples are impacted by industrial resource development that takes place on, or near, their communities. Existing literature on impacts of industrial resource development on Indigenous Peoples primarily focus on physical health outcomes and rarely focus on the mental health impacts. To understand the full range of long-term and anticipated health impacts of industrial resource development on Indigenous communities, mental health impacts must be examined. It is well-established that there is a connection between the environment and Indigenous wellbeing, across interrelated dimensions of mental, physical, emotional, and spiritual health. METHODS: This paper identifies how the Community Advisory Team and a team of Indigenous and settler scholars will conduct the review. The literature search will use the OVID interface to search Medline, Embase, PsycINFO, and Global Health databases. Non-indexed peer-reviewed journals related to Indigenous health or research will be scanned. Books and book chapters will be identified in the Scopus and PsycINFO databases. The grey literature search will also include Google and be limited to reports published by government, academic, and non-profit organizations. Reference lists of key publications will be checked for additional relevant publications, including theses, dissertations, reports, and other articles not retrieved in the online searches. Additional sources may be recommended by team members. Included documents will focus on Indigenous Peoples in North America, South America, Australia, Aotearoa New Zealand, and Circumpolar regions, research that reports on mental health, and research that is based on land loss connected to dams, mines, agriculture, or petroleum development. Literature that meets the inclusion criteria will be screened at the title/abstract and full-text stages by two team members in Covidence. The included literature will be rated with a quality appraisal tool and information will be extracted by two team members; a consensus of information will be reached and be submitted for analysis. DISCUSSION: The synthesized evidence from this review is relevant for land use policy, health impact assessments, economic development, mental health service planning, and communities engaging in development projects. SYSTEMATIC REVIEW REGISTRATION: Registered in the International Prospective Register of Systematic Reviews (PROSPERO; Registration number CRD42021253720 ).
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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 | 0.090 | 0.088 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.063 | 0.008 |
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".