Intention to quit betel quid: a comparison of betel quid chewers and cigarette smokers.
Bibliographic record
Abstract
BACKGROUND: Despite the global significance of betel quid chewing and the associated health risks, there have been no studies assessing chewers' intention to quit. Given the difficulties associated with quitting betel quid and the serious health consequences of chewing, it is important for researchers to develop interventions aimed at helping chewers quit. Betel quid chewers experience similar patterns of dependence and withdrawal symptoms as tobacco smokers, and the use of both substances causes serious adverse health effects. Therefore, it is possible that intention to quit betel quid and tobacco would also be similar. If similarities were found, researchers could look to existing tobacco cessation interventions to inform the development of betel quid cessation interventions. In the current study we sought to understand chewers' intention to quit and how it compares to smokers' intention to quit cigarettes. METHODS: A total of 351 adult betel quid chewers from Guam were compared against 1,555 adult tobacco users from Hawaii. These comparisons were made possible because of the deliberate use of identical questionnaire items (mutatis mutandis) for betel quid chewing and cigarette smoking. RESULTS: Smokers reported higher levels of wanting to quit, intending to quit, and wishing they have never started in the first place compared to chewers (p's<.0001). There were no differences across groups with regard to having a plan for how to quit and when to quit, with half of the samples reporting not having a plan for how or when to quit. CONCLUSION: Both smokers and chewers want to quit and intend to quit, but do not have plans of when or how to quit. A deeper understanding of chewers' intention to quit and its similarities to smokers' intention to quit could be used to inform the development of betel quid cessation interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".