Indigenous youth mental wellness and the adaptation of the JoyPopTM app (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED To date, Indigenous youth in Canada face significant socio-political risks and threats to their mental wellness, such as intergenerational trauma, water insecurity, environmental degradation, and lack of safe housing. Given the need for mental wellness resources for Indigenous youth, there has been a rising interest in technology-based interventions and mobile applications. The JoyPopTM app is a mobile mental health tool, shown to reduce depression and increase emotions management. We conducted 19 consultations (32% male) about the JoyPopTM app with key adult community stakeholders from the Six Nations of the Grand River Community, with the aim of gathering feedback (e.g., general thoughts and suggestions for improvement) about the app as a resilience tool for Indigenous youth. Consultations were coded in a double-blind fashion and analyzed for emerging themes. Overall, the majority of consultants (>50%) spontaneously offered positive feedback about the app, with significant enthusiasm for the social connecting elements (e.g., the Circle of Trust feature). Also, importance was given to the need to incorporate traditional colours and design elements (e.g., beadwork, nature backgrounds, etc.), as well as cultural practices (e.g., inclusion of new features/activities related to nature, adding Indigenous sounds to the SleepEase activity, etc.). Suggested changes and improvements will be taken into consideration for future adaptations to the JoyPopTM app, to develop a more culturally relevant app that better supports Indigenous youth mental wellness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".