Literature DB >> 17441818

Use of social network analysis to describe service links for farmers' mental health.

Jeffrey Fuller1, Brian Kelly, Gina Sartore, Lynne Fragar, Anne Tonna, Georgia Pollard, Trevor Hazell.   

Abstract

OBJECTIVE: The primary mental health care needs of farmers require that service innovations incorporate rural support workers into a local service network. This component of the FarmLink pilot sought to develop a social network analysis method that would describe local mental health-related human service networks. The purpose is to inform improvements in this network and to serve as a baseline against which such improvements can be evaluated.
DESIGN: A pilot survey of rural human service providers who deal with mental health-related issues among farmers about their self-reported links between each other.
SETTING: Service delivery agencies associated with a small rural town in New South Wales. PARTICIPANTS: Twenty-five agents from a range of human services involved in rural human support services to farmers, such as from agricultural and drought support, welfare, primary health care and education. INTERVENTION: Telephone interview prior to the conduct of a Mental Health First Aid seminar and a Farmers Mental Health and Wellbeing workshop. MAIN OUTCOME MEASURES: Agent self-reported service links over the past three months for information exchange, client referrals and working together in relation to helping farmers for mental health, emotional health or stress-related problems. Analysis trialled on the 'made referrals' link shows the network influence, prominence and intermediary status of the rural financial counsellor.
CONCLUSIONS: Within the limitations of recalled self-report data, social network analysis provides a useful network description for informing and evaluating service network improvements.

Entities:  

Mesh:

Year:  2007        PMID: 17441818     DOI: 10.1111/j.1440-1584.2007.00861.x

Source DB:  PubMed          Journal:  Aust J Rural Health        ISSN: 1038-5282            Impact factor:   1.662


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