Types of smokers in a community sample of individuals with Type 2 diabetes: a latent class analysis
Bibliographic record
Abstract
AIMS: Despite the detrimental effects of smoking on their health, a high number of adults with Type 2 diabetes continue to smoke. Identifying distinct types of smokers within this population could help tailor and target intervention programmes. This study examined whether smokers with Type 2 diabetes could be classified into different profiles based on smoking habits, socio-economic characteristics and lifestyle factors. METHODS: A 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. Information related to smoking, socio-economic status, health and lifestyle was collected by phone interview at baseline and 1 year later. Latent class analysis was used to identify subgroups of smokers. RESULTS: We uncovered three meaningful classes of smokers: class 1, long-time smokers with long-standing diabetes (n = 105); class 2, heavy smokers with deprived socio-economic status, poor health and unhealthy lifestyle characteristics (n = 105); class 3, working and active smokers who were more recently diagnosed with diabetes (n = 173). Members of class 2 were significantly more likely to be disabled and depressed at baseline and 1 year later compared with other classes. CONCLUSIONS: Different profiles of smokers exist among adults with Type 2 diabetes, each suggesting different cessation treatment needs. Distinguishing between these types of smokers may enable clinicians to tailor their approach to smoking cessation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".