Tobacco-Smoking, Alcohol-Drinking, and Betel-Quid-Chewing Behaviors: Development and Use of a Web-Based Survey System
Bibliographic record
Abstract
BACKGROUND: Smoking tobacco, drinking alcohol, and chewing betel quid are health-risk behaviors for several diseases, such as cancer, cardiovascular disease, and diabetes, with severe impacts on health. However, health care providers often have limited time to assess clients' behaviors regarding smoking tobacco, drinking alcohol, and chewing betel quid and intervene, if needed. OBJECTIVE: The objective of this study was to develop a Web-based survey system; determine the rates of tobacco-smoking, alcohol-drinking, and betel-quid-chewing behaviors; and estimate the efficiency of the system (time to complete the survey). METHODS: Patients and their family members or friends were recruited from gastrointestinal medical-surgical, otolaryngology, orthopedics, and rehabilitation clinics or wards at a medical center in northern Taiwan. Data for this descriptive, cross-sectional study were extracted from a large series of research studies. A Web-based survey system was developed using a Linux, Apache, MySQL, PHP stack solution. The Web survey was set up to include four questionnaires: the Chinese-version Fagerstrom Tolerance Questionnaire, the Chinese-version Alcohol Use Disorders Identification Test, the Betel Nut Dependency Scale, and a sociodemographic form with several chronic diseases. After the participants completed the survey, the system automatically calculated their score, categorized their risk level for each behavior, and immediately presented and explained their results. The system also recorded the time each participant took to complete the survey. RESULTS: Of 782 patient participants, 29.6% were addicted to nicotine, 13.3% were hazardous, harmful, or dependent alcohol drinkers, and 1.5% were dependent on chewing betel quid. Of 425 family or friend participants, 19.8% were addicted to nicotine, 5.6% were hazardous, harmful, or dependent alcohol drinkers, and 0.9% were dependent on chewing betel quid. Regarding the mean time to complete the survey, patients took 7.9 minutes (SD 3.0; range 3-20) and family members or friends took 7.7 minutes (SD 2.8; range 3-18). Most of the participants completed the survey within 5-10 minutes. CONCLUSIONS: The Web-based survey was easy to self-administer. Health care providers can use this Web-based survey system to save time in assessing these risk behaviors in clinical settings. All smokers had mild-to-severe nicotine addiction, and 5.6%-12.3% of patients and their family members or friends were at risk of alcohol dependence. Considering that these three behaviors, particularly in combination, dramatically increase the risk of esophageal cancer, appropriate and convenient interventions are necessary for preserving public health in Taiwan.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".