P2-94 Types of smokers, depression and disability in type 2 diabetes: a latent class analysis
Bibliographic record
Abstract
Despite the detrimental effects of smoking on health, a high number of adults with type 2 diabetes continue to smoke. Identifying distinct profiles of smokers could help tailor smoking intervention programs in this population and may help uncover high risk subgroups with unfavourable health outcomes. This study examined whether smokers with type 2 diabetes could be classified into different profiles based on socioeconomic characteristics, smoking habits and lifestyle factors. Depression and disability outcomes were compared across smoking profiles. A community sample of adults with self-reported diabetes was selected from random digit dialing. Analyses included 383 participants with type 2 diabetes who were current smokers. Participants were interviewed at baseline (2008) and re-interviewed 1 year later (2009). Latent class analysis was used to identify types of smokers. We uncovered three meaningful classes of smokers: (1) long-time smokers with long-standing diabetes (n=105), (2) heavy smokers with deprived socioeconomic status, poor health and unhealthy lifestyle characteristics (n=105), (3) working and active smokers, recently diagnosed with diabetes (n=173). Members of class 2 were significantly more likely to be disabled and depressed at baseline and follow-up compared with others. They were also less likely to have quit smoking at follow-up, despite attempting to quit as often as others. Different profiles of smokers exist among adults with type 2 diabetes. One class of smokers is particularly linked with depression, disability and a deprived socioeconomic situation. Distinguishing between types of smokers may enable clinicians to tailor their approach to smoking cessation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".