Smoking cessation delivery by general practitioners in Crete, Greece
Bibliographic record
Abstract
Background: Tobacco dependence treatment in clinical settings is of prime public health importance, especially in Greece, a country experiencing one of the highest rates of tobacco use in Europe. Methods: Our study aimed to examine the characteristics of tobacco users and document rates of tobacco treatment delivery in general practice settings in Crete, Greece. A cross-sectional sample of patients (n = 2, 261) was screened for current tobacco use in 25 general practices in Crete, Greece in 2015/16. Current tobacco users completed a survey following their clinic appointment that collected information on patient characteristics and rates at which the primary care physician delivered tobacco treatment using the evidence-based 4 A's (Ask, Advise, Assist, Arrange) model during their medical appointment and over the previous 12-month period. Multi-level modeling was used to analyze data and examine predictors of 4 A's delivery. Results: Tobacco use prevalence was 38% among all patients screened. A total of 840 tobacco users completed the study survey [mean age 48.0 (SD 14.5) years, 57.6% male]. Approximately, half of the tobacco users reported their general practitioner 'asked' about their tobacco use and 'advised' them to quit smoking. Receiving 'assistance' with quitting (15.7%) and 'arranging' follow-up support (<3%) was infrequent. Patient education, presence of smoking-related illness, a positive screen for anxiety or depression and the type of medical appointment were associated with 4 A's delivery. Conclusion: Given the fundamental importance of addressing tobacco treatment, increasing the rates of 4 A's treatment in primary care settings in Greece is an important target for improving patient care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".