Knowledge gaps about smoking cessation in hospitalized patients and their doctors
Bibliographic record
Abstract
BACKGROUND: Hospitalization is an opportune time for smoking cessation support; cessation interventions delivered by hospital physicians are effective. While general practitioners' and outpatients' knowledge and attitudes towards smoking cessation have been studied in great detail, in-patient cessation programmes have received less attention. DESIGN: Questionnaire-based survey of a convenience sample of hospital physicians and in-patients at Göttingen University Hospital, Germany. METHODS: All 159 physicians directly involved in bedside care on medical and surgical wards received a three-page questionnaire examining smoking status, knowledge of smoking-attributable morbidity and mortality, and their understanding of the effectiveness of methods to achieve long-term smoking cessation. Perceived barriers to the delivery of counselling and cessation services to smoking patients were identified. One thousand randomly selected patients on medical (N = 400) and surgical (N = 600) wards were invited to complete a similar questionnaire. RESULTS: Seventy-seven physicians (response rate 48.4%) and 675 patients (67.5%) completed the questionnaire. Patients and physicians alike underestimated the smoking-attributable risk of developing smoking-related cancers and chronic obstructive lung disease. In addition, severe misperceptions regarding the effectiveness of cessation methods were noted in both populations with 'willpower' being thought to be most effective in achieving abstinence. Only one-third of smoking patients recalled having been counselled to quit. Physicians identified lack of time as a central barrier to counselling smoking patients. CONCLUSIONS: These findings suggest that hospitalized smokers in a large German university hospital might not be treated according to international guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.020 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".