Opinions of Teachers’ Students toward Teachers Who Smoked in Thailand: A Qualitative Study
Bibliographic record
Abstract
The purpose of this qualitative research was to describe the opinions of teachers’ students toward teachers who smoked in Thailand. The population was teachers’ students (N=30) in their 1st–5th academic year of the Faculty of Education who participated based on purposive sampling according to eligibility criteria. The QDA Miner Lite program was used to analyze the qualitative data. The results indicated that the teachers’ students understanding of a teacher was a person who conveys knowledge, experience and life skills in various fields for students both inside and outside the classroom and school. They should be a good person with knowledge and qualifications, displaying exemplary maturity in every way and be a role model. Like a family that gives love and keeps training and teaching in the right way. A teacher should be a good adviser and listen to problems. The ideal teacher must love to teach proficiently and be knowledgeable in what is being taught, allowing students to understand various forms of knowledge and be current. Smoking also affects being a teachers’ students, in terms of inappropriateness and was not suitable for future teachers as it could affect the image of the organization. It also has harmful effect on health. A method that is likely to help prevent smoking among teachers’ students and teachers is to create awareness through training and organizing various projects such as establishing a smoking cessation treatment center and campaigning for ways to replace smoking or to refrain from smoking.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".