Training General Practitioners in Evidence-Based Tobacco Treatment: An Evaluation of the Tobacco Treatment Training Network in Crete (TiTAN-Crete) Intervention
Bibliographic record
Abstract
BACKGROUND: Rates of tobacco treatment delivery in primary care are suboptimal. AIMS: We report on the effectiveness of the TiTAN Crete intervention on rates of patient-reported 4As (ask, advise, assist, arrange) tobacco treatment and general practitioner's (GP) knowledge, attitudes, self-efficacy, and intentions. METHODS: A quasi-experimental pilot study with pre-post evaluation was conducted in Crete, Greece (2015-2016). GPs ( n = 24) intervention and control group and a cross-sectional sample of their patients ( n = 841) were surveyed before the implementation of the intervention. GPs in the intervention group received training, practice, and patient tools to support the integration of the 4As treatment into clinical routines. Intervention group GPs ( n = 14) and a second cross-sectional sample of patients ( n = 460) were surveyed 4 months following the intervention to assess changes in outcomes of interest. Multilevel modeling was used to analyze data. RESULTS: Among GPs exposed to the intervention, significant increases in knowledge, self-efficacy, and rates of 4As delivery were documented between the pre- and postassessment and compared with those of the control group. Specifically, the adjusted odds ratios (AORs) and 95% confidence intervals (CIs) for 4As delivery between the pre-and postassessment among GPs exposed to the TiTAN intervention were as follows: Ask AOR 3.66 (95% CI [2.61, 5.14]); Advise AOR 4.21 (95% CI [3.02, 5.87]); Assist AOR 13.10 (95% CI [8.83, 19.42]) and Arrange AOR 4.75 (95% CI [2.67, 8.45]). CONCLUSION: We found significant increases in rates at which GPs delivered evidence-based tobacco treatment following exposure to the TiTAN intervention. Future research should examine methods for supporting broader dissemination of well-designed training interventions in general practice.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".