Adapting Evidence-Based Tobacco Addiction Treatment for Inuit Living in Ontario: A Qualitative Study of Collaboration and Co-creation to Move From Pan-Indigenous to Inuit-Specific Programming
Bibliographic record
Abstract
Settler introduction of tobacco to Inuit Nunangat (homeland of Inuit in Canada) has led to high tobacco use prevalence among Inuit. Inuit are moving from traditional territories to the province of Ontario to access resources, including health services. Indigenous-specific tobacco cessation approaches in Ontario lack cultural relevance among Inuit, as they often reflect First Nations and Métis worldviews. To improve effectiveness of tobacco cessation services for Inuit living in Ontario, materials reflective of Inuit culture and worldviews were developed through a community-based participatory approach. The Centre for Addiction and Mental Health collaborated with Tungasuvvingat Inuit and members of an Engagement Circle who work with Inuit or identify as Inuk (n = 25) to initiate a knowledge translation project aimed at co-creating a toolkit of Inuit-specific cessation resources. Development was guided by Two-Eyed Seeing, whereby Inuit and Western worldviews come together to support a strengths-based approach. The toolkit was evaluated through a pilot session and focus group with Inuit living in Ottawa who use tobacco (n = 13) and an online survey administered with a group of helpers who work with Inuit (n = 11). Analysis of qualitative data from the focus group and online survey highlighted five themes: choice, cultural relevance and safety, capacity-building, access, and impact. Focus group participants reported they learned quitting was possible and identified new strategies to quit through the session. Our findings emphasize the importance of engagement and co-creation with Indigenous Peoples to ensure cultural relevance and appropriateness of healthcare interventions.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".