Application of Two-Eyed Seeing in Adolescent Mental Health to Bridge Design Thinking and Indigenous Collective Storytelling
Bibliographic record
Abstract
BACKGROUND: eMental health apps are increasingly being considered for use in health care with growing recognition of the importance of considering end-user preferences in their design and implementation. The key to the success of using apps with Indigenous youth is tailoring the design and content to include Indigenous perspectives. In this study we used a Two-Eyed Seeing perspective to integrate Indigenous and human computer interaction methodologies to identify end-user preferences for a tablet-based mental health screening app used in a primary care clinic serving Indigenous youth. OBJECTIVE: The research objectives used a Two-Eyed Seeing approach to (i) collectively create stories about Indigenous youth lived experiences accessing integrated primary care for their mental health concerns; and (ii) engage Indigenous youth in Design Circles to determine their usability preferences for digital mental health screening tools. METHOD: Eight adolescents (n = 4 young women; n = 3 young men; and n = 1 Two Spirit) between 20 to 24 years old who self-identified as Indigenous participated. Indigenous youth joined Design Circles to co-create a story about accessing mental health care and their needs and preferences for an eMental Health app. RESULTS: Findings highlighted the importance of collective Indigenous storytelling about accessing integrated primary care for mental health needs. Participants created three persona stories about their challenges accessing mental health care and the role of social support. Participants sorted their usability design preferences for an eMental Health app to be inclusive of Indigenous knowledges. CONCLUSIONS: A Two-Eyed Seeing perspective was useful to incorporate a design thinking approach as collective storytelling among Indigenous youth. This research may inform and shape the design of eMental health apps used in health clinics to better engage Indigenous youth.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".