Impact of marginalization on tobacco use in individuals diagnosed with head and neck Cancer
Bibliographic record
Abstract
BACKGROUND: Considerable evidence now indicates that individuals living in underprivileged neighbourhoods have higher rates of mortality and morbidity independent of individual-level characteristics. This study explored the impact of geographical marginalization on smoking cessation in a population of individuals with a diagnosis of head and neck cancer. The aims of this study were twofold: (1) assess the prevalence of smoking cessation in those with a previous diagnosis of head and neck cancer, (2) analyze the determinants of smoking alongside area-based measures of socioeconomic status. METHODS: This was a cross-sectional study. We administered a self-reported nicotine dependence package to participants between the ages of 20-90 with a previous mucosal head and neck cancer diagnosis and with a history of tobacco use. Using the Canadian Marginalization (CAN-Marg) Index tool based on 2006 Canada Census data we compared the degree of marginalization to the smoking status. For those individuals who were currently smoking, nicotine dependence and readiness to quit were assessed. A summative score of marginalization was compared to smoking status of individuals. RESULTS: The results from this study indicate that the summative level of marginalization developed from the combined factors of residential instability, material deprivation, ethnic concentration and dependency may be important factors in smoking cessation. CONCLUSIONS: This analysis of determinants of smoking alongside area-based measures of socioeconomic status may implicate the need for targeted population-based smoking cessation 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".