Dance for Wellness: Indigenous adolescents’ Perspectives on Mental Health, Wellness, and Dance
Bibliographic record
Abstract
PURPOSE: Indigenous adolescents living on reservations in Canada experience mental health disparities, and there is an urgent need to address this as mental illness and suicide increase. Therefore, a dance program was initiated to attempt to improve mental health and wellness for Indigenous adolescents The reaearch purpose was to gain insight into theexperiences of mental health and wellness in Indigenous adolescents and identify if and how a four-week dance program affected their mental health and wellness. DESIGN: A trauma-informed, qualitative, single-case study design was used. METHOD: Semi-structured interviews and symbol-based data were used to interview eight adolescents aged 11 - 16 years living in a Denesuliné community from February to June 2022. FINDINGS: Three multidimensional themes emerged: It Helped My Mental Health and Everything (Dance Helps Me Escape, Dance Makes Me Feel Good, and Dance Is Just Really Fun); A Little Bubble Around Me When I Dance (I Can Be More Confident and I Can Be Myself), and We Won't Judge You Here (Creation of Friendship, He Said I Was Really Good, and Improved School Attendance). CONCLUSIONS: The dance program impacted the participants' mental health and wellness by providing positive activities to engage the adolescents. CLINICAL EVIDENCE: Community health nurses (CHNs) assist in promoting health and wellness in Indigenous adolescents living on reservation and can use these findings to advocate for funding for the development of dance-based mental health and wellness programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".