Counseling Chinese patients about cigarette smoking: the role of nurses
Bibliographic record
Abstract
Purpose The main purpose of this study is to determine the cigarette smoking rate and smoking cessation counseling frequency in a sample of Chinese nurses. Design/methodology/approach At the time of data collection, the hospital had 260 nurses, 255 females and five males. The 200 nurses working on the two daytime shifts were given the questionnaires; none refused to participate, reaching a response rate of 100 percent. All the participants were females as the five male nurses were working in the operation rooms at the time of data collection, are were thus not accessible. Findings Some key findings include: only two nurses, out of 200, identified themselves as current cigarette smokers; all provided anti‐smoking counseling to patients, the majority of them did not think their efforts were successful; cigarette smoking is a problem in China: the nurses estimated that 80 percent of male and 10 percent of female patients were current smokers; in the opinions of the nurses, Chinese smokers used smoking as a stress reliever and a social lubricant; two methods may help smokers to quit or reduce smoking: using aids such as patches, acupuncture and nicotine gum, and counseling by health professionals; the nurses think that cigarette smoking is well accepted in the Chinese culture. Practical implications Findings of this research suggest that the Chinese Ministry of Health should take measures to change the cultural norms and values regarding cigarette smoking including strict rules be imposed on not passing/sharing cigarettes in the workplace. Originality/value In a collectivistic culture such as China where opinions of authorities are respected, the part of nurses, who represent health authority to their patients, in assisting patients to quit or reduce smoking cannot be overemphasized. This study adds to the scarce research on Chinese nurses' role in helping patients' smoking cessation efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".