Climate change, biodiversity loss, and Indigenous Peoples’ health and wellbeing: a systematic umbrella review protocol
Bibliographic record
Abstract
BACKGROUND: Research that examines the intersections of Indigenous Peoples' health and wellbeing with climate change and biodiversity loss is abundant in the global scholarship. A synthesis of this evidence base is crucial in order to map current pathways of impact, as well as to identify responses across the global literature that advance Indigenous health and wellbeing, all while centering Indigenous voices and perspectives. This protocol details our proposed methodology to systematically conduct an umbrella review (or review of reviews) of the synthesized literature on climate change, biodiversity loss, and the health and wellbeing of Indigenous Peoples globally. METHODS: A multidisciplinary team of Indigenous and non-Indigenous scholars will conduct the review, guided by an engagement process with an Indigenous Experts group. A search hedge will be used to search PubMed®, Scopus®, Web of Science™, CINAHL (via EBSCOHost®), and Campbell Collaboration databases and adapted for use in grey literature sources. Two independent reviewers will conduct level one (title/abstract) and level two (full-text) eligibility screening using inclusion/exclusion criteria. Data will be extracted from included records and analyzed using quantitative (e.g., basic descriptive statistics) and qualitative methods (e.g., thematic analysis, using a constant comparative method). DISCUSSION: This protocol outlines our approach to systematically and transparently review synthesized literature that examines the intersections of climate change, biodiversity loss, and Indigenous Peoples' health and wellbeing globally. SYSTEMATIC REVIEW REGISTRATION: This protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on April 24, 2023 (registration number: CRD42023417060).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".