Psychological factors associated with knowing an Indigenous language
Bibliographic record
Abstract
Indigenous peoples in Canada continue to face ongoing negative impacts of colonialism and related prejudice, discrimination, and racism; however, connections to Indigenous culture and language have been shown to support resilience and mental wellbeing. This study investigated whether and how knowing an Indigenous language relates to Indigenous peoples' mental health and why. It was hypothesised that Indigenous peoples who speak an Indigenous language would report better mental health and that both belongingness and collective self-esteem would mediate this relationship. Statistical mediation was used to assess these hypotheses with a subsample ( n = 19,509) of the 2017 Aboriginal Peoples Survey. As expected, knowing an Indigenous language had significant and positive indirect effects on mental health via both belongingness and collective self-esteem. Respondents who spoke an Indigenous language felt a greater sense of belonging and were more positive about their Indigenous identity. These correlational findings suggest that learning an Indigenous language could be an effective means of improving mental health. Although the study design did not allow for causal conclusions, most respondents learned the Indigenous language in childhood, so the predictor preceded the outcome. Acknowledging the limits of correlational research, it was concluded that the findings imply that Indigenous peoples' mental health may be improved through language programming. Language may have this effect because it weaves together individuals with their communities and cultures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".