ArticleSSM - population health2017
Best-worst scaling survey of preferences regarding the adverse effects of tobacco use in China.
Article in SSM - population health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Analyzing Strawberry Preferences: Best-Worst Scaling Methodology and Purchase Styles.Foods (Basel, Switzerland) · 2024Article
- Best-Worst Scaling Survey of Inpatients' Preferences in Medical Decision-Making Participation in China.Healthcare (Basel, Switzerland) · 2023Article
- The role of socio-demographic variables and buying habits in determining milk purchasers' preferences and choices.Frontiers in nutrition · 2023Article
- Civil society's perception of forest ecosystem services. A case study in the Western Alps.Frontiers in psychology · 2022Article
- A Study on China's Tobacco Taxation and Its Influencing Factor on Economic Growth.Frontiers in psychology · 2022Article
- Investigating Medical Student's Preferences for Internet-Based Healthcare Services: A Best-Worst Scaling Survey.Frontiers in public health · 2021Article
- Consumer Attitudes and Preference Exploration towards Fresh-Cut Salads Using Best-Worst Scaling and Latent Class Analysis.Foods (Basel, Switzerland) · 2019Article
- A study on Chinese consumer preferences for food traceability information using best-worst scaling.PloS one · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We use best-worst scaling to assess two types of concern levels of the adverse consequences of smoking in China. While the smoking cessation policy has worked well in Taiwan, more than 1 million people in mainland China are estimated to die every year from tobacco use. This study compares the preferences of Chinese individuals in the two jurisdictions (mainland versus Taiwan) and explores the possibility of information-based interventions. The relative importance of 13 adverse effects was assessed by conducting a web-based survey on a sample of 480 Chinese participants. The 13 items consist of various adverse effects of tobacco use: from long-term health risk, such as lung cancer and cardiovascular diseases, to reduction of physical capacity and sexual dysfunction, and disturbance to non-smokers. The resulting data suggest possible strategies to curb smoking. Subgroup analysis, focusing on gender, smoking status, and nicotine dependence, was also conducted. Lung cancer, cardiovascular diseases, and chronic obstructive pulmonary disease, in this order, rank highest for both types of respondents. On the other hand, high expenditures (13th) and weight gain after cessation (12th) are the lowest ranked for both. Measuring individual best-worst scores reveals substantial heterogeneity among respondents and that information-based intervention can help curb smoking.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.