Tobacco treatment TrAining Network in Crete (TiTAN-Crete): protocol for a controlled before-after study
Bibliographic record
Abstract
INTRODUCTIONRates of tobacco use in Greece are among the highest in Europe and are responsible for an enormous burden of chronic disease and death.A large proportion of tobacco users report an interest in quitting.Family medicine practices have been identified as important settings for identifying tobacco users, delivering advice to quit smoking, and providing tobacco treatment interventions.The 5A's (ask, advice, assess, assist, arrange) schema is an evidence-based model for addressing tobacco use in clinical settings.The rates at which primary care clinicians in Greece address tobacco use in their practice is unknown but, as in other countries, is understood to be sub-optimal.This paper describes the rationale, design, and protocol for a pre/post, controlled study to compare the effectiveness of a smoking cessation intervention delivered in primary care practices in Heraklion, Greece.The TiTAN-Crete intervention includes a 1-day tobacco treatment training program, dissemination of provider and patient resources and two booster training sessions.Participating providers and a cross-sectional sample of patients from their practices, will be surveyed.Outcome measures include changes in provider attitudes and beliefs, perceived behavioral control, intentions, and barriers related to smoking cessation treatment delivery.Rates at which providers deliver tobacco treatment to patients will be measured by patient report of 5As delivery.Multi-level modeling will be used to examine the effects of the intervention.This study will lead to a better understanding of how to best assist clinicians in Greece to enhance the rates at which smoking cessation treatments are delivered to smokers.
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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 | 0.043 | 0.028 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.097 | 0.020 |
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".