Laughter Yoga as a School-based Wellness Program: Supporting the Well-Being of Nishnawbe Youth: Supporting the Well-Being of Nishnawbe Youth
Bibliographic record
Abstract
This paper explores how the participation in Laughter Yoga (LY) could assist in supporting the overall well-being of Nishnawbe youth. Many Nishnawbe youth are at a heightened risk of mental health issues and social inequities that are associated with the (social) stigma and discrimination that is indicative of colonialism. I illustrate these risks and inequities by discussing the effects of colonialism and the Indian Residential Schools. I discuss the educational inequities that impact many First Nations youth and review the province of Ontario’s largest coroner’s inquest into the tragic deaths of seven Nishnawbe youth in the Canadian city of Thunder Bay as an example of these inequities. I then provide an Indigenous perspective of mental health in Canadian schools and introduce how the use of laughter has been recognized by Indigenous groups around the world as an integral component of community bonding, social interaction, and communal storytelling. Next, I examine the positive physiological and psychological affects that laughter has on the body and how the promotion of laughter is one strategy that could be introduced to advocate an overall sense of wellness. I then explain the concept of LY and the benefits that LY could have in the classroom. This paper concludes with a list of recommendations that will help support educational administrators, educators, and those who work with/for First Nations youth in the implementation of a school-based LY program as an embodied movement wellness practice with/for First Nations youth within Canadian schools.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".