The Use of Mobile Applications to Support Indigenous Youth Wellbeing in Canada
Bibliographic record
Abstract
In Canada, Indigenous youth have remained resilient despite being confronted with a wide range of structural and systemic risks, such as long-lasting boil water advisories, over-representation in the child welfare system, and injustices related to land treaties. As people of the land, all disruptions to ecological health are a disruption to personal and community holistic health. Land-based activities and cultural continuity strengthen pathways of perseverance for Indigenous youth (Toombs et al., 2016). For youth, cultural self-expression and personal agency are enhanced with digital platforms, which are well-suited to Indigenous people’s strengths in art, music, and oral forms of passing on knowledge. The field of mental health has turned to e-supports such as mobile applications (apps) that can provide easy-to-access intervention, when needed. To date, resilience interventions have received comparatively less attention than the study of resilience factors and processes. It is timely to review the extant literature on mental health apps with Indigenous youth as, currently, Indigenous apps are in early research stages. Critically reviewing work to date, it is argued that an inclusive and expansive concept of resilience, coherent with Indigenous holistic health views, is well-positioned as a foundation for collaborative resilience app development. To date, few mental health apps have been researched with Indigenous youth, and fewer have been co-constructed with Indigenous youth and their community members. The current literature points to feasibility in terms of readiness or potential usage, and functionality for promoting an integrated cultural and holistic health lens. As this effort may be specific to a particular Indigenous nation’s values, stories, and practices, we highlight the Haudenosaunee conceptual wellness model as one example to guide Indigenous and non-Indigenous science integration, with a current project underway with the JoyPopTM mHealth app for promoting positive mental health and resilience.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".