Supporting Smokers in Difficult Settings: Suggestions for Better Education and Counseling in Cancer Centers in Jordan
Bibliographic record
Abstract
Continued smoking in cancer patients is commonly observed in Jordan. In a country that exhibits some of the highest smoking rates globally, enhancing patient education regarding the value of smoking cessation for cancer care is vital. The objectives of our study were to describe sociodemographic and clinical factors associated with continued smoking in Jordanian smokers after a cancer diagnosis; to identify reasons for smoking and knowledge regarding smoking's impact on care; to examine in a multivariable manner the factors associated with continued smoking, and to accordingly generate patient counseling recommendations. An interviewer-administered survey using the Theoretical Domains Framework was employed. Among 350 subjects (mean age 51.0, median 52.7), approximately 38% of patients had quit or were in the process of quitting; 61.7% remained smokers. Substantial knowledge gaps with regard to the impact of continued smoking on cancer care were observed. Remaining a smoker after diagnosis was associated with being employed, not receiving chemotherapy or surgery, having lower confidence in quitting, and having a lower number of identified reasons for smoking. Interventions to promote cessation in Jordanian cancer patients who smoke should focus on enhancing patient awareness about the impact of smoking in cancer care and raising perceived self-efficacy to quit.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".