Theory Driven Research Design for Explaining Behavioural Health Risk Transitions: The Case of Smoking  

Oleh:

Dixon, Jane ; Banwell, Cathy

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Social Science & Medicine (www.elsevier.com/locate/sosscimed) vol. 68 no. 12 (Jun. 2009), page 2206-2214.
Topik: Bourdieu; Social Contagion and Mimesis; Health Risk Transitions; Cigarette Consumption; Smoking

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: SS53 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Recent social network analyses have suggested that common chronic disease risk factors are more mutable than expected; raising practical considerations for public health interventions. Within this context, it is timely to assess the alternative social science reasoning being offered to explain behavioural health risk transitions. This paper takes up this challenge by critically reviewing the major theories applied to the temporal trends and sub-population variations in affluent country smoking behaviour. Three explanations dominate: a materialist approach; Bourdieu's distinctive class-based cultures; and, the spread of norms and emotions within social networks. We note conceptual tension when integrated theories are adopted. We also report on the relative absence of theoretical interrogation for the persistent adoption of smoking behaviours among present and successive lower socio-economic status (SES) cohorts. While unequal rates of persistence within cohorts has received some attention, the ongoing adoption of a non-innovative and health damaging behaviour is not well understood. To this end, we suggest the incorporation of several underused concepts: namely Bourdieu's 'rules of the game' and 'symbolic violence' and 'mimesis', an aspect of social contagion. We conclude by describing the implications for social action of the alternative theories, and argue that theory driven research designs could deliver more efficacious evidence for interventions than the post hoc application of theories to existing data sets.

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