Physician-Based Tobacco Smoking Cessation Counseling in Belgrade, Serbia.
Bibliographic record
Abstract
Introduction The risk and burden of tobacco smoking on illness (e.g., several cancers, chronic obstructive pulmonary disease, and cardiovascular diseases) and death is well established. (1,2) About five million people die from smoking-related illnesses each year, with over half of these deaths occurring in the age range 30 to 69 years. (3) Yet, studies have shown that physician counseling of patients can substantially reduce smoking rates. (4-9) Physician counseling can have both a positive impact on a patient's desire to quit smoking, as well as on their follow-up commitment to remain tobacco free. (10) In a study among adults in Ontario, Canada, about two thirds viewed physicians as a very good source of advice on quitting smoking. (11) A Cochrane database system review found that brief advise intevention can increase smoking cessation by 3%, beyond an unassisted quit rate of 2 to 3%. Additional benefit, albeit small, occurred with follow-up visits.10 In another review study, intensive smoking advise from physicians, bupropion, and nicotine replacement therapy were confirmed as effective smoking cessation interventions in adults. (12) In a U.S. study, patients who were prescribed nicotine replacement therapy and received smoking cessation advice and support from their physician were significantly more likely to quit smoking than those who only received nicotine replacement therapy. (13) Thus, increased involvement of physicians in counseling patients about smoking is an important strategy for lowering smoking-related illness and death. Limited physician time is a major obstacle for providing counseling and follow-up in primary care. (14) In addition, health-care workers often feel inadequately trained and unequipped with the proper resources to effectively counsel patients about smoking. (15-17) In a prospective study involving outpatients in Switzerland, only 28% of the patients received information about the risk of smoking, and only 10% discussed cessation. (18) Further, physicians who smoke tend to be less likely to counsel their patients about smoking or initiate cessation interventions. (19,20) Little is currently known about tobacco-cessation counseling provided by physicians in Serbia, and how this practice corresponds with the physicians' own training on counseling patients about smoking. Purpose of Study This preliminary study will identify the percentage of physicians who regularly counsel their patients about smoking, the percentage of physicians trained in patient counseling, and how training corresponds to patient counseling. In addition, physician counseling of patients about smoking will be examined according to physician cessation training and physician smoking status. Methods Subjects This preliminary study focused on practicing physicians in Belgrade, Serbia. Belgrade is the capital of the country with its own autonomous city government. It is the central economic hub of Serbia and the leader in Serbian culture, education, and science. Approximately 21% of the country's population lives in Belgrade. (21,22) Analyses were based on a sample of 86 physicians, practicing in selected public and private health care facilities in Serbia. Key administrators in each hospital were contacted to obtain approval to administer the questionnaire. Public and private health care facilities and a large hospital in Belgrade were selected (Institut za Zdravstvenu Zastitu Majke i Deteta Srbije and Klinicki Urgenti Centar Srbije; Dom Zdravlja, Novi Beograd). These facilities were conveniently selected, although believed to provide a good representation of physicians in Belgrade, Serbia. The administrators of each facility provided a verbal agreement of their willingness to participate. The numbers of physicians who completed questionnaires in the two sample groups were 50 and 36, respectively. Instruments and Procedures The questionnaire consisted of four sections. …
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".