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Record W4401856221 · doi:10.1101/2024.08.20.24310741

Linguistic vitality improves health and wellbeing in Indigenous communities: a scoping review

2024· review· en· W4401856221 on OpenAlexaffabout
Louise Harding, Ryan DeCaire, Karleen Delaurier-Lyle, Ursula Ellis, Julia Schillo, Mark Turin

Bibliographic record

VenuemedRxiv · 2024
Typereview
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsVitalityIndigenousSociologyPsychologyLinguisticsEcologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract Introduction While Indigenous communities have long recognized the importance of their languages for their wellbeing, this topic has only recently received attention in scholarship, research and public policy. This scoping review synthesizes and assesses existing literature on the links between Indigenous linguistic vitality and health or wellness in English-speaking settler colonial countries (Australia, Canada, New Zealand, and the United States). Methods The JBI methodology for scoping reviews was followed by an interdisciplinary research team. 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. 78% of the original research studies report only supportive links between Indigenous languages and health, while 98% of the literature reviews report supportive links. Linguistic vitality tends to support health and wellness outcomes, while the diminishment of languages is associated with worse health. The most prevalent links with linguistic vitality are healthcare outcomes, overall health and healing, and mental, cognitive, and psychological health and development. The results of the remaining original research studies were mixed (10%), statistically non-significant (6%), adverse (5%) and neutral (1%). Conclusions The results of this scoping review suggest that linguistic vitality 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 promotion, and advancing knowledge through funding relevant community-engaged research and education. Article Highlights Our interdisciplinary authorship team conducted a scoping review involving the screening of nearly 10,000 publications. 262 academic and grey literature reports were published between 1949-2019 which had a central focus on exploring the links between Indigenous languages and health. 78% of original research studies and 98% of reviews report exclusively supportive links between linguistic vitality and the health and wellness of Indigenous peoples. Key areas linked to health include healthcare outcomes, overall health and healing, and mental, cognitive, and psychological health and development. Linguistic vitality is a determinant of health for Indigenous peoples and should be leveraged through supporting language programs, delivering linguistically tailored care, and funding community-engaged research and education.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.025
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0100.009
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.062
GPT teacher head0.425
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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