Inflammatory Bowel Disease in Indigenous Populations: A Scoping Review.
Bibliographic record
Abstract
Background: Globally, inflammatory bowel disease (IBD) rates have surged; however, Indigenous populations are underrepresented in research and face unique healthcare challenges due to socioeconomic barriers. We aimed to synthesize available literature on IBD in Indigenous populations worldwide, identify research gaps, and propose recommendations to improve research inclusivity. Methods: A literature search was conducted across 8 online databases: MEDLINE, EMBASE, CINAHL, SCOPUS, and others. We included qualitative, quantitative, and mixed-method research, alongside commentaries, editorials, and abstracts published since 1962 in English focused on IBD in Indigenous populations. The studies were critically appraised and summarized. Findings and recommendations for future research from the perspective of Indigenous patient partners were presented. Results: = 4), with single studies from Chile and the United States. Indigenous populations were found to have lower rates of IBD compared to the general population; however, some studies reported recently increasing rates, potentially resulting from urbanization, dietary changes, and other environmental factors. Canadian studies highlighted barriers faced by Indigenous peoples in accessing care. Notably, only 3 articles demonstrated Indigenous engagement. Conclusions: This review highlights gaps in the literature about IBD in Indigenous populations. While the prevalence of IBD among Indigenous peoples is low, rates may be rising. Further research should continue studying the rising rates of IBD in Indigenous populations, alongside contributing genetic and environmental factors. Indigenous peoples must be included as research partners, and Indigenous research methodologies must be adhered to.
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 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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".