Learning from history: How research evidence can inform policies to improve rural and remote medical workforce distribution

John Humphreys, John Wakerman

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

Objective: This article describes the rationale for, and development of, an evidence-based rural typology as the basis for an incentive scheme to improve medical workforce retention. This case study describes the key factors associated with ensuring that research evidence is translated into rural health policy and program implementation. Design: Case study. Setting: Rural and remote Australia. Results: The development and implementation of the Modified Monash Model in Australia demonstrates some of the key facilitators of knowledge exchange. These include: sound evidence based on good empirical data and rigorous methodology; transparency to peers and stakeholders; the importance of long-term commitment to rural health research; the credibility of the researcher; multiple modes of communication of results; real world validation and amplification of results; patience and persistence underscored by the commitment of researchers to achieving more equitable outcomes for rural and remote doctors; serendipity in terms of timing; and a governmental culture that values evidence-based outcomes. Conclusion: The knowledge exchange process is neither easy nor simple and rarely rapid. It often requires the engagement of communities, professional associations, health care providers, researchers, policy-makers and funders. Implementation of evidence into rural health policies and programs benefits all parties through significant improvements in efficiency, effectiveness and equity.

Original languageEnglish
Pages (from-to)329-334
Number of pages6
JournalThe Australian Journal of Rural Health
Volume26
Issue number5
DOIs
Publication statusPublished - 1 Oct 2018

Keywords

  • policy
  • program evaluation
  • rural and remote services
  • rural service planning
  • workforce planning

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