Exploring the Perceptions of Cannabis Use Among Indigenous Youth in Manitoba Through Their Lived Experiences, Photovoice and Indigenous Research Methodologies: A Research Protocol
Bibliographic record
Abstract
Introduction: In light of the increasing prevalence of cannabis use among Indigenous youth in Manitoba, Canada, this study seeks to address a critical gap in research by leveraging Indigenous methodologies and perspectives. The legalization of cannabis in Canada, intended to reduce juvenile use, has not fully achieved its goal with youth, particularly Indigenous youth who experience heightened exposure and associated risks. This research aims to illuminate the lived experiences of Indigenous adolescents. Methods: The study will recruit 20 Indigenous youth in Manitoba with experiences of cannabis use using snowball and purposeful sampling methods. Integrating community-based Indigenous research methodologies and frameworks: community-engaged scholarship (CES), participatory action research (PAR), Indigenous knowledge (IK), community-based participatory research (CBPR), photovoice, open-ended interviews, and storytelling. By centering Indigenous knowledge systems and engaging directly with the youth, the study aims to develop a nuanced understanding of their motivations, cultural and community influences, and the impact of cannabis use on their lives. Indigenous methodologies ensure that the research process is respectful, culturally sensitive, and beneficial to the community. This approach honors traditional knowledge and facilitates meaningful dialogue between researchers and participants. Anticipated Results: The analysis will employ a modified constructivist grounded theory (MCGT) approach. This methodology will involve an inductive data analysis to identify patterns, categories, and themes that emerge directly from the data. The results will be systematically organized using NVivo software. Researchers will code the transcripts openly to identify recurring themes, patterns, and potential subthemes and then structure them into a coherent framework. Discussion: By incorporating the unique perspectives of Indigenous youth, the study aims to inform evidence-based strategies more aligned with their specific needs and experiences. Conclusion: Ultimately, the goal is to enhance policy-making efforts and create supportive frameworks that can better address and mitigate cannabis use among Indigenous youth in Manitoba.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".