• breast cancer survivor;
  • health behaviour;
  • Health Belief Model;
  • health promotion;
  • Precede–Proceed model;
  • Uncertainty in Illness Theory

tramm r., mccarthy a. & yates p. (2011) Using the Precede–Proceed Model of Health Program Planning in breast cancer nursing research. Journal of Advanced Nursing68(6), 1870–1880.


Aim.  In this article, we discuss the use of the Precede–Proceed model when investigating health promotion options for breast cancer survivors.

Background.  Adherence to recommended health behaviours can optimize well-being after cancer treatment. Guided by the Precede–Proceed approach, we studied the behaviours of breast cancer survivors in our health service area.

Data sources.  The interview data from the cohort of breast cancer survivors are used in this article to illustrate the use of Precede–Proceed in this nursing research context. Interview data were collected from June to December 2009. We also searched Medline, CINAHL, PsychInfo and PsychExtra up to 2010 for relevant literature in the English language to interrogate the data from other theoretical perspectives.

Discussion.  The Precede–Proceed model is theoretically complex. The deductive analytic process guided by the model usefully explained some of the health behaviours of cancer survivors, although it could not explicate many other findings. A complementary inductive approach to the analysis and subsequent interpretation by way of Uncertainty in Illness Theory and other psychosocial perspectives provided a comprehensive account of the qualitative data that resulted in contextually relevant recommendations for nursing practice.

Implications for nursing.  Nursing researchers using Precede–Proceed should maintain theoretical flexibility when interpreting qualitative data. Perspectives not embedded in the model might need to be considered to ensure that the data are analysed in a contextually relevant way.

Conclusion.  Precede–Proceed provides a robust framework for nursing researchers investigating health promotion in cancer survivors; however, additional theoretical lenses to those embedded in the model can enhance data interpretation.