Engaging Indigenous youth through popular theatre: Knowledge mobilization of Indigenous peoples’ perspectives on access to healthcare services
Bibliographic record
Abstract
In Canada, Indigenous peoples bear a greater burden of illness and suffer disproportionate health disparities compared to non-Indigenous people. Difficult access to healthcare services has contributed to this gap. In this article, we present findings from a dissemination grant aimed to engage Indigenous youth in popular theatre to explore inequities in access to health services for Indigenous people in a Western province in Canada. Following an Indigenous and action research approach, we undertook popular theatre as a means to disseminate our research findings. Popular theatre allows audience members to engage with a scene relevant to their own personal situation and to intervene during the performance to create multiple ways of critically understanding and reacting to a difficult situation. Using popular theatre was successful in generating discussion and engaging the community and healthcare professionals to discuss next steps to increasing access to healthcare services. Popular theatre and short dramas provide a venue for mirroring stigmatized care and expose racial biases in the delivery of care. The contributions of the students, their input, and their acting were to increase our awareness even more of the pervasiveness of the stigmatized care that Indigenous people experience.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".