Language improves health and wellbeing in Indigenous communities: A scoping review
Bibliographic record
Abstract
Introduction Indigenous languages in Australia, Canada, New Zealand and the United States are endangered due to colonial policies which promote English language dominance. While Indigenous communities know the importance of language for their wellbeing , this topic has only recently received attention in scholarship and public policy. This scoping review synthesizes and assesses existing literature on the links between the vitality of Indigenous languages and health or wellness in four English-speaking settler colonial countries. Methods Our interdisciplinary research team followed JBI methodology for scoping reviews. Key databases searched included MEDLINE, PsycInfo, and Cumulative Index to Nursing and Allied Health Literature. Searches were restricted to English language literature. The last search was on February 8, 2021. Quantitative and qualitative analyses were conducted to categorize and elucidate the nature of the links reported. Results Over 10,000 records were reviewed and 262 met the inclusion criteria – 70 % academic and 30 % gray literature. The largest number of studies focus on Canadian contexts (40.1 %). 78 % of the original research studies report only supportive links between Indigenous languages and health, while 98 % of the literature reviews report supportive links. The most significant aspects of health reported to be positively related to language are outcomes from health care , education and promotion initiatives; overall health, wellness, resilience and healing; and mental, cognitive, and psychological health and development. The results of the remaining original research studies are mixed (10 %), statistically non-significant (6 %), adverse (5 %) and neutral (1 %). Conclusions The results of this scoping review suggest that a vast body of academic and gray literature exists to support that language is a determinant of health for Indigenous peoples in the contexts studied. Recommendations for harnessing the healing effects of language include increasing tangible support to language programs, delivering linguistically tailored health care , and advancing knowledge through community-engaged research and education.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".