Stakeholder analysis of tobacco control policy in Iran: a qualitative study
Bibliographic record
Abstract
Tobacco control in low- and middle-income countries is a complex issue due to powerful economic and political bodies in the production and distribution of tobacco products. The urgent need for effective tobacco control measures is highlighted by the rising health burden associated with tobacco use in Iran, necessitating a comprehensive stakeholder analysis. The current study aimed to analyze the roles and characteristics of all stakeholders involved in tobacco control policymaking in Iran. Adhering to the theoretical framework introduced by Varvasovsky and Brugha, a qualitative stakeholder analysis was conducted based on five items of stakeholders’ roles, interests, positions, powers and influences. The actors included in the study encompassed governmental bodies, non-governmental organizations (NGOs), the tobacco industry, health organizations, and the international community. The data were collected using semi-structured interviews with 36 key informants. MAXQDA V.10 software was used to analyze interviews by content analysis, and PolicyMaker software V.4 was used to perform data analysis. Forty-two tobacco-related stakeholders were identified and classified into eight groups of governmental executives, legislative body, non-governmental organizations, mass media, universities, clerics and imitators, the tobacco industry, and international organizations. The Iranian Parliament and public sector stakeholders have the central role in all stages of the policymaking process. The state actors prevail in all stages of Iran’s tobacco control policymaking. The number of non-governmental organizations active in the field of tobacco control was limited. Notably, there exists a conflict of interest between the actors in the two areas of tobacco control and the tobacco industry. Health-related organizations in Iran exhibit a weak coalition for tobacco control, lacking influence over the tobacco industry and smuggling control actors, while various stakeholders have diverse interests impacting their relationships. Strengthening the government’s tobacco control capacity and developing a national tobacco control strategy, in which intersectoral collaboration between different actors is established, can reduce the conflict of interests between involved actors in Iran. This, in turn, leads to a reduction in the smoking rate and an improvement in public health in Iran.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".