Application of a Poster and Slogan Campaign to Prevent Smoking, in Conjunction with a Health Education Program
Bibliographic record
Abstract
Adolescent smoking is dangerous. This study examined the self-assessment of risk factors to expose student teachers to smoking, compared the scores of health belief model structures in experimental and control groups, and summarized the poster and slogan campaign to prevent smoking. The experimental and control groups (n=30 each) were selected using eligibility criteria and simple random sampling. Three-part questionnaires were used to collect information that was analyzed based on mean, minimum, maximum, percentage, and paired sample t-test data. The key findings were: 1) student teachers in the experimental and control groups had smoked 1–2 cigarettes in the past (20% and 6.67%, respectively). In addition, for both the groups, cigarettes were readily available at convenience stores in residential areas (63.34% and 83.34%, respectively), their fathers smoked (16.67% and 20%, respectively), and their peers persuaded them to smoke (16.67 % and 23.30 %, respectively); 2) the health belief model demonstrated that both the experimental and control groups at 8 weeks had comparable pre-post findings for susceptibility (p=.000 and p=0.049, respectively), perceived severity (p=.000 and p=0.063, respectively), perceived benefits (p=.000 and p=0.065, respectively), perceived barriers (p=.000 and p=0.703, respectively), and cure to action (p=.000 and p=0.070, respectively); and 3) The main study "Online Health Education Program to Prevent Tobacco Use for Student Teachers during COVID-19 Pandemic in Thailand: Design, Challenges, and Outcomes" worked on anti-smoking posters and slogans. The 8-week education program with events could enhance experimental group health beliefs. Thus, student teacher smoking prevention efforts should incorporate posters and slogans.
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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".