Knowledge, attitudes, and behaviours towards smoking among people with migration experience: a global scoping review
Bibliographic record
Abstract
Abstract Background Knowledge, attitudes, and behaviours towards smoking have been extensively researched across diverse populations with migration experience. The objective of this scoping review was to understand the extent, type, and geographical distribution of the published literature on smoking among people with migration experience across the globe. Methods We conducted a scoping review using the Joanna Briggs Institute methodology. Supported by two information specialists, we performed a comprehensive literature search (from 2012- 17 January 2024) in five databases, without language or geographic restrictions. The search yielded 8,400 potentially relevant records after deduplication. After title and abstract screening, 305 full texts were included for descriptive analysis and out of these, 25 that covered all three aspects (knowledge, attitudes, and behaviour) were included for content analysis. Results The review identified a predominance of quantitative studies (87%), with a smaller proportion of qualitative (12%) and mixed-methods studies (2%). Most studies focused on behavioural prevalence related to tobacco and nicotine product consumption, with 72% addressing behaviours specifically, though only a subset (26%) focused directly on tobacco and/or nicotine use among migrants. Geographical analysis revealed that most of the research originated from high-income countries in particular the USA (n = 126), Canada (n = 32), and Germany (n = 20), with notable gaps in regions with significant migrant populations, such as Saudi Arabia (n = 1). Among the 25 KAB-focused studies, data collection was based on surveys (52%), interviews (40%), and focus groups (28%). Findings showed varying knowledge of tobacco harms, shaped by socio-economic status, acculturation, and health literacy. Attitudes were influenced by culture and religion, while smoking behaviours were driven by gender, stress, peer influence, and migration-related pressures. Terminology varied across studies, with inconsistent definitions for key terms "migrants" and "immigrants," complicating comparisons between populations and countries. Definitions of tobacco products also varied across studies. Conclusions This scoping review reveals significant gaps in research on migrants' knowledge, attitudes, and behaviours towards tobacco and nicotine consumption, including a lack of qualitative studies, inconsistent terminology, and a geographic focus on high-income countries. Addressing these gaps through expanded research in underrepresented regions and standardising terminology is essential for developing culturally relevant public health strategies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.084 | 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".