Smoking cessation counselling practices of family physicians in Jordan
Bibliographic record
Abstract
Objectives: To assess the smoking cessation counselling practices of family physicians in Jordan and assess their perception about the availability of smoking cessation resources and about the barriers to effective smoking cessation practices. Methods: A pre-structured questionnaire was distributed to 124 family physicians practicing in teaching and Ministry of Health medical centres in Jordan. All participants were asked about their smoking cessation practices and about the barriers to effective smoking cessation practices. Results: Only 39.8% reported that they assess the willingness of the patients to quit smoking and 28.2% reported that they discuss counselling options with smokers. Considerably fewer percentages of physicians reported that they prepare their patients for withdrawal symptoms (11.6%), discuss pharmacotherapies (4.9%), describe a nicotine patch (5.0%), and provide patients with self-help materials (6.7%). The two factors cited most often by physicians as significant barriers to smoking cessation counselling were lack or too few available cessation programmes (90.3%) and limited training for physicians on tobacco and cessation interventions (90.3%). Conclusion: While a high proportion of Jordanian family physicians reported that they usually ask patients about smoking status and advise them to stop smoking, they do not regularly provide extensive assistance to help their patients to quit smoking. Lack or too few available cessation programmes and limited training for physicians on smoking cessation interventions were identified as the two major barriers to effective smoking cessation counselling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".