Bibliographic record
Abstract
The aim of this study was to explore smokers' responses to a 100% tax increase on tobacco prices in Saudi Arabia. According to the World Health Organisation, an increase in tax is the single most effective tool to reduce the incidence of smoking. However, using a tax increase in other health areas (especially in reducing alcohol, sugar, or fat consumption) shows that consumers' reactions can differ according to socio-demographic characteristics. 334 participants of different generational cohorts and incomes completed a questionnaire. It was found that only about 10% of the participants actually reduced their consumption to maintain smoking the same brand, 20% changed to cheaper brands, and over 60% made no changes at all to their smoking behaviours. Age and income played very minor roles. Interestingly, a majority claimed that they were considering quitting smoking, the percentages dropping from 75% of the younger respondents to 56% of the older. The research shows that socio-demographic features play a large part in smokers' behaviour changes in response to a large tax increase on cigarettes. It recommends setting up a minimum price for all brands that could discourage young people from starting to smoke.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".