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Record W2955938011 · doi:10.1186/s12889-019-7223-1

Factors influencing the implementation of a pilot smoking cessation intervention among migrant workers in Chinese factories: a qualitative study

2019· article· en· W2955938011 on OpenAlexaff
Guanyang Zou, Xiaolin Wei, Simin Deng, Jia Yin, Li Ling

Bibliographic record

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersMedical Research Council
KeywordsMedicineIntervention (counseling)Smoking cessationThematic analysisContext (archaeology)Psychological interventionQualitative researchBiostatisticsEnvironmental healthPublic healthMigrant workersNursingEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Tobacco control intervention with Chinese internal migrants, especially those working in factories has rarely been investigated. This study aims to identify aids and barriers to implementing a comprehensive pilot intervention aimed at reducing smoking among migrant workers working in factories in China. METHOD: Twenty in-depth interviews were conducted 3 months into the intervention, with managers, migrant workers and team leaders in two factories, where the pilot intervention was implemented, in Zhongshan city in Guangdong, a southern Chinese province. Data analysis was based on the thematic approach. RESULTS: This study identifies the societal, individual and programmatic factors that could influence the implementation of a pilot smoking cessation intervention among migrant workers in the two Chinese factories. At the societal level, social customs and relationships where smoking is seen as essential in social communications was the most important barrier to the implementation of smoking cessation intervention. At the individual level, migrant-related features such as low education, high mobility and poor integration with local residents, together with poor health beliefs and attitudes added to the challenges of implementing smoking cessation intervention. At the programmatic level, the role of small-team leaders was generally positive, although limited due to their busy work patterns and poor powers of enforcement. CONCLUSION: Achieving successful smoking cessation intervention in factories could be challenging with many migrants, as multi-level factors including social context, intervention delivery, individual and migrants' characteristics play an important role in shaping the implementation of the intervention. Our study suggests the importance of tailoring interventions for the migrant factory workers. TRIAL REGISTRATION: ChiCTR-OPC-17011637 at Chinese Clinical Trial Registry. Retrospectively registered on 12th June 2017.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.101
GPT teacher head0.439
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2019
Admission routes1
Has abstractyes

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