Women and Waterpipe Tobacco Smoking in the Eastern Mediterranean Region: Allure or Offensiveness
Bibliographic record
Abstract
The prevalence of waterpipe tobacco smoking is increasing worldwide, despite evidence indicating its adverse health effects. Women and young people seem more likely to be choosing waterpipe tobacco smoking over cigarettes. The objective of this qualitative study was to understand better whether and why waterpipe smoking is a more acceptable form of tobacco use than cigarettes for women in the Eastern Mediterranean Region, and to understand whether the strategies used by multi-national corporations to attract women to cigarette smoking were perceived to be relevant in the context of waterpipe tobacco use. Focus groups (n = 81) and in-depth interviews (n = 38) were conducted with adults in Lebanon, Egypt, Palestine, and Syria. Discussions were thematically analyzed and recurrent themes identified. One of the themes which emerged was the negative image of women smoking waterpipes. Moreover, the sexual allure conveyed through waterpipe smoking as well as waterpipe tobacco smoking as a symbol of emancipation was illustrated. The latter was mainly expressed in Lebanon, in contrast with Egypt where traditional gender roles depict women smoking waterpipes as disrespectful to society. Understanding the social aspects of waterpipe tobacco smoking is crucial to planning future interventions to control waterpipe tobacco smoking among women and in society at large.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".