Hospital doctors' attitudes toward giving their patients smoking cessation help
Bibliographic record
Abstract
BACKGROUND AND AIMS: While guidelines recommend all doctors to ask their patients about their smoking habits and, in case they smoke, offer cessation advice, limited data are available about hospital doctors' attitudes toward these recommendations. We aimed to examine hospital doctors' attitudes toward asking their patients about their smoking habits, informing about the health benefits of stopping, and offering help to quit smoking. MATERIALS AND METHODS: A random sample (n = 1025) of Norwegian hospital doctors was mailed a questionnaire on this topic. After two reminders, 76% responded. RESULTS: Among the respondents 23% of the doctors found it too time consuming to ask if the patient smoked, and approximately 35% found it too time consuming to inform or offer help on smoking cessation. About 25% of the doctors felt that they did not possess enough knowledge to help the patient to stop smoking, and 65% of the doctors preferred to refer to a specialist for this. Twenty-eight per cent of the doctors did not see it as their task to help the patient to stop smoking, while 32% did not think it is worth the effort to offer the patient help to stop smoking. Twice as many non-internists as internists regarded it as not their task to ask about smoking and advised on smoking cessation. CONCLUSION: In conclusion, about one-quarter to one-third of Norwegian hospital doctors seem to disagree with current guidelines that all doctors should address their patients' smoking habits.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".