DIGITAL TECHNOLOGIES, MENTAL HEALTH CHALLENGES AND ACADEMIC LANGUAGE DEVELOPMENT OF INDIGENOUS YOUTH: A RETROSPECTIVE
Bibliographic record
Abstract
Indigenous students have experienced negative inter-generational impacts from colonization and socioeconomic stress, leading to mental health challenges and persistent subpar academic performance.Both issues intertwined pose a complex challenge that has been increasingly documented by media, research, and in government reports and has had a significant impact on Indigenous youth's wellbeing and academic achievement.In addition to the educational disparity faced by Indigenous youth, particularly those living in remote Indigenous communities, high rates of suicide, depression, and substance abuse have prevented them from obtaining the language and literacy skills required for graduating high school and pursuing post-secondary education and professional opportunities.Educational interventions would be more effective if these issues were addressed in their design and implementation and grounded in Indigenous cultural and community practices.Research has reported that many Indigenous youth have adopted or are keen to adopt digital technologies, which have the potential to provide e-mental health resources as well as opportunities to improve academic literacy skills.This research synthesis examines the evidence of the efficacy of using digital technologies to support Indigenous youth's mental health and the learning of language and literacy skills.It presents a profile of important studies focusing on Indigenous youth's perspectives on both issues.Based on a culturally responsive pedagogical framework, this article provides insights for teaching practice, and also identifies gaps for future research and instructional innovations that are urgently needed to support Indigenous youth students.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".