Banning shisha smoking in public places in Iran: an advocacy coalition framework perspective on policy process and change
Bibliographic record
Abstract
INTRODUCTION: Shisha smoking is a widespread custom in Iran with a rapidly growing prevalence especially among the youth. In this article, we analyze the policy process of enforcing a federal/state ban on shisha smoking in all public places in Kerman Province, Iran. Guided by the Advocacy Coalition Framework (ACF), we investigate how a shisha smoking ban reached the political agenda in 2011, how it was framed by different policy actors, and why no significant breakthrough took place despite its inclusion on the agenda. METHODS: We conducted a qualitative study using a case study approach. Two main sources of data were employed: face-to-face in-depth interviews and document analysis of key policy texts. We interviewed 24 policy actors from diverse sectors. A qualitative thematic framework, incorporating both inductive and deductive analyses, was employed to analyze our data. RESULTS: We found that the health sector was the main actor pushing the issue of shisha smoking onto the political agenda by framing it as a public health risk. The health sector and its allies advocated enforcement of a federal law to ban shisha smoking in all public places including teahouses and traditional restaurants whereas another group of actors opposed the ban. The pro-ban group was unable to neutralize the strategies of the anti-ban group and to steer the debate towards the health harms of shisha smoking. Our analysis uncovers three main reasons behind the policy stasis: lack of policy learning due to lack of agreement over evidence and related analytical conflicts between the two groups linked to differences in core and policy beliefs; the inability of the pro-ban group to exploit opportunities in the external policy subsystem through generating stronger public support for enforcement of the shisha smoking ban; and the nature of the institutional setting, in particular the autocratic governance of CHFS which contributed to a lack of policy learning within the policy subsystem. CONCLUSIONS: Our research demonstrated the utility of ACF as a theoretical framework for analyzing the policy process and policy change to promote tobacco control. It shows the importance of accounting for policy actors' belief systems and issue-framing in understanding how some issues get more prominence in the policy-making process than others. Our findings further indicate a need for significant resources employed by the state through public awareness campaigns to change public perceptions of shisha smoking in Iran which is a deeply anchored cultural practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".