Health effects of Indigenous language use and revitalization: a realist review
Bibliographic record
Abstract
BACKGROUND: Indigenous populations across the world are more likely to suffer from poor health outcomes when compared to other racial and ethnic groups. Although these disparities have many sources, one protective factor that has become increasingly apparent is the continued use and/or revitalization of traditional Indigenous lifeways: Indigenous language in particular. This realist review is aimed at bringing together the literature that addresses effects of language use and revitalization on mental and physical health. METHODS: Purposive bibliographic searches on Scopus were conducted to identify relevant publications, further augmented by forward citation chaining. Included publications (qualitative and quantitative) described health outcomes for groups of Indigenous people who either did or did not learn and/or use their ancestral language. The geographical area studied was restricted to the Americas, Australia or New Zealand. Publications that were not written in English, Spanish, French, Portuguese or German were excluded. A realist approach was followed to identify positive, neutral or negative effects of language use and/or acquisition on health, with both qualitative and quantitative measures considered. RESULTS: The bibliographic search yielded a total of 3508 possible publications of which 130 publications were included in the realist analysis. The largest proportion of the outcomes addressed in the studies (62.1%) reported positive effects. Neutral outcomes accounted for 16.6% of the reported effects. Negative effects (21.4%) were often qualified by such issues as possible cultural use of tobacco, testing educational outcomes in a student's second language, and correlation with socioeconomic status (SES), health access, or social determinants of health; it is of note that the positive correlations with language use just as frequently occurred with these issues as the negative correlations did. CONCLUSIONS: Language use and revitalization emerge as protective factors in the health of Indigenous populations. Benefits of language programs in tribal and other settings should be considered a cost-effective way of improving outcomes in multiple domains.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".