Our Health Counts Toronto: Commercial tobacco use among Indigenous peoples in Toronto
Bibliographic record
Abstract
OBJECTIVE: Fueled by the commercial tobacco industry, commercial tobacco use continues to be the leading preventable cause of premature death in Canada, with opportunities to improve health outcomes. The objective of this research was to work with Indigenous partners to generate Indigenous population prevalence estimates of commercial tobacco use in Toronto, and examine the association between smoking and sociodemographic, cultural, resiliency, and social variables. METHODS: Respondent-driven sampling (RDS) was used to generate prevalence estimates of commercial tobacco use and potentially associated sociodemographic, cultural, resiliency, and social connection variables for Indigenous adults living in Toronto. Statistical analysis examined associations between smoking and variables theorized to be predictors of tobacco use. RESULTS: The findings indicated that 36.3% (95%CI 28.2-44.5) of the Indigenous population in Toronto do not smoke, and 63.6% (95%CI 55.5-71.7) reported smoking. Univariate analysis of demographic, social, and cultural variables found age and employment to be statistically significantly different between adults who smoked and adults who did not smoke. Indigenous adults who were above the before-tax low-income cut-off (LICO) were more likely to smoke compared to those who were below the before-tax LICO. Indigenous adults who completed high school were more likely to smoke compared to those who did not complete high school, similarly to those who were unemployed compared to those who were employed. However, those who were not in the labour force (student or retired) were less likely to smoke compared to those who were employed. These effects remained after adjustment for age, gender, and LICO. Indigenous adults with stable housing were 20% less likely to smoke compared to those experiencing homelessness. Adults who had at least one close friend or family member to confide in were more likely to smoke compared to those who did not have any close friends or family members. Indigenous adults were more likely to smoke if they participated in Indigenous ceremony compared to those who did not participate. CONCLUSION: The Indigenous population in Toronto continues to experience smoking prevalence nearly four times greater than that in the general population. This highlights the need for accurate population data to inform programs and policies and address the social determinants of health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".