Journal of Population Research

, Volume 27, Issue 1, pp 15–42 | Cite as

Semantic analysis of natural language processing in a study of nurse mobility in the Northern Territory, Australia

  • Kostas Alexandridis
  • Kristal Coe
  • Stephen Garnett


In lieu of diverse consequences in the demand and supply of health care professionals such as nurses and midwives in Australia and the world, a firm understanding of the characteristics of staff mobility and the factors influencing their retention could lead to achieving enhanced service delivery, greater job satisfaction, and the establishment of a more stable and robust workforce. The research reported in this paper attempts to shed light on qualitative aspects of mobility in health care professional staff in the Northern Territory of Australia. It builds upon an existing survey study of the quantitative factors that determine why nurses and midwives come to the Northern Territory, why some stay and why many leave, by analysing additional qualitative textual responses of participants using semantic network approaches to natural language processing. Our results illustrate the methodological and policy significance of semantic approaches to knowledge acquisition and representation, especially in complementing findings of traditional survey analysis techniques, and in analysing the broader social settings, effects and consequences of staff retention and mobility.


Semantic analysis Semantic networks Knowledge representation Health care professionals Population mobility Nurses and midwives Northern Territory, Australia 



We are most grateful to our co-workers in this research: Kate Golebiowska, Helen Walsh and Kerstin Zander at Charles Darwin University and Steven Guthridge, Shu Qin Li and Rosalyn Malyon from the Northern Territory Department of Health and Families. We are also deeply indebted to Greg Rickard of the Northern Territory Department of Health and Families and Dean Carson and Tony Barnes Charles Darwin University for their advice and guidance throughout this study.


  1. Alexandridis, K., & Maru, Y. (in review). Collapse and reorganization patterns of social knowledge representation in evolving semantic networks. Information Sciences.Google Scholar
  2. Alexandridis, K., Maru, Y., Davies, J., Box, P., & Hueneke, H. (2009). Constructing semantic knowledge networks from the ground up: Livelihoods and employment outcomes in Anmatjere region, central Australia. In R. S. Anderssen, R. D. Braddock & L. T. H. Newham (Eds.), 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation (pp. 2819–2825). Cairns, Australia, 13–17 July 2009: Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation.Google Scholar
  3. Anderson, J. R. (1983). A spreading activation theory of memory. Journal of Verbal Learning and Verbal Behavior, 22, 261–295.CrossRefGoogle Scholar
  4. Atmanspacher, H., & Filk, T. (2006). Complexity and non-commutativity of learning operations on graphs. BioSystems, 85(1), 84–93.CrossRefGoogle Scholar
  5. Australian Bureau of Statistics (ABS). (2006). National regional profile: Northern Territory.
  6. Baldi, P., & Rosen-Zvi, M. (2005). On the relationship between deterministic and probabilistic directed Graphical models: From Bayesian networks to recursive neural networks. Neural Networks, 18(8), 1080–1086.CrossRefGoogle Scholar
  7. Bales, M. E., & Johnson, S. B. (2006). Graph theoretic modeling of large-scale semantic networks. Journal of Biomedical Informatics, 39(4), 451–464.CrossRefGoogle Scholar
  8. Barabási, A.-L. (2003). Linked: How everything is connected to everything else and what it means for business, science and everyday life. New York: Plume Books, Penguin.Google Scholar
  9. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.CrossRefGoogle Scholar
  10. Blakemore, A. E., & Hoffman, D. L. (1989). Seniority rules and productivity: An empirical test. Economica, New Series, 56(223), 359–371.CrossRefGoogle Scholar
  11. Blutner, R. (2004). Nonmonotonic inferences and neural networks. Synthese, 142(2), 143–174.CrossRefGoogle Scholar
  12. Bomberger, N. A., Waxman, A. M., Rhodes, B. J., & Sheldon, N. A. (2007). A new approach to higher-level information fusion using associative learning in semantic networks of spiking neurons. Information Fusion, 8(3), 227–251.CrossRefGoogle Scholar
  13. Brewka, G., Benferhat, S., & Le Berre, D. (2004). Qualitative choice logic. Artificial Intelligence, 157(1–2), 203–237.CrossRefGoogle Scholar
  14. Carley, K. M., & Krackhardt, D. (1996). Cognitive inconsistencies and non-symmetric friendship. Social Networks, 18(1), 1–27.CrossRefGoogle Scholar
  15. Carvalho, L. A. V. (1999). Modeling distributed concept representation in Hopfield neural networks. Mathematical and Computer Modelling, 30(1–2), 225–242.CrossRefGoogle Scholar
  16. Chiu, C., Gruber, S. A., Simpson, N., & Yurgelun-Todd, D. A. (2003). Indirect semantic priming in schizophrenic patients. Schizophrenia Research, 60(1), 129.CrossRefGoogle Scholar
  17. Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407–428.CrossRefGoogle Scholar
  18. Crestani, F. (1997). Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11(6), 453–482.CrossRefGoogle Scholar
  19. Dahlgren, K. (1995). A linguistic ontology. International Journal of Human-Computer Studies, 43(5–6), 809–818.CrossRefGoogle Scholar
  20. Dix, J., Jantke, K. P., & Schmitt, P. H. (1991). Nonmonotonic and inductive logic: 1st international workshop, Karlsruhe, Germany, December 47, 1990, proceedings. Berlin, New York: Springer.Google Scholar
  21. Edalat, A. (2000). Domain theory in learning processes. Electronic Notes in Theoretical Computer Science, 1, 1–18.CrossRefGoogle Scholar
  22. Fellbaum, C. (1998). WordNet: An electronic lexical database. Cambridge, MA: MIT Press.Google Scholar
  23. Friedkin, N. E. (1991). Theoretical foundations for centrality measures. The American Journal of Sociology, 96(6), 1478–1504.CrossRefGoogle Scholar
  24. Garnett, S. T., Coe, K., Golebiowska, K., Walsh, H., Zander, K. K., Guhridge, S., et al. (2008). Attracting and keeping nursing professionals in an environment of chronic labour shortage: A study of mobility among nurses and midwives in the Northern Territory of Australia. Darwin: Charles Darwin University Press.Google Scholar
  25. Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18(12), 1069–1076.CrossRefGoogle Scholar
  26. Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California.Google Scholar
  27. Harris, M., NTong, K.-K., & Tseng, Y.-P. (2002). Optimal employee turnover rates: Theory and evidence (Report No. 19/02). Melbourne: The University of Melbourne.Google Scholar
  28. Hendler, J. A. (1988). Integrating marker-passing and problem-solving: A spreading activation approach to improved choice in planning (p. 307). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  29. Karmel, T., & Li, J. (2002). The nursing workforce 2010. Canbera: Department of Education Science and Training.Google Scholar
  30. Kim, K.-M., Hong, J.-H., & Cho, S.-B. (2007). A semantic Bayesian network approach to retrieving information with intelligent conversational agents. Information Processing & Management, 43(1), 225–236.CrossRefGoogle Scholar
  31. Lee, C. S., Kao, Y. F., Kuo, Y. H., & Wang, M. H. (2007). Automated ontology construction for unstructured text documents. Data & Knowledge Engineering, 60(3), 547–566.CrossRefGoogle Scholar
  32. Magnini, B., & Strapparava, C. (2004). User modelling for news web sites with word sense based techniques. User Modeling and User-Adapted Interaction, 14(2–3), 239–257.CrossRefGoogle Scholar
  33. Mika, P. (2007). Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web, 5(1), 5–15.CrossRefGoogle Scholar
  34. Nerbonne, J. A. (1998). Linguistic databases. Stanford, CA: CSLI Publications.Google Scholar
  35. Newman, M. E. J. (2001). Clustering and preferential attachment in growing networks. Santa Fe Institute.Google Scholar
  36. Ormerod, P., & Colbaugh, R. (2006). Cascades of failure and extinction in evolving complex systems. Journal of Artificial Societies and Social Simulation, 9(4), 9.Google Scholar
  37. Oulton, J. A. (2006). The global nursing shortage: An overview of issues and actions. Policy Politics Nursing Practice, 7(3, suppl), 34S–39S.CrossRefGoogle Scholar
  38. Palla, G., Barabási, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136), 664–667.CrossRefGoogle Scholar
  39. Pinkas, G. (1995). Reasoning, nonmonotonicity and learning in connectionist networks that capture propositional knowledge. Artificial Intelligence, 77(2), 203–247.CrossRefGoogle Scholar
  40. Pomi, A., & Mizraji, E. (2004). Semantic graphs and associative memories. Physical Review E, 70(6), 066136-066131-066136-066136.Google Scholar
  41. Quillian, M. R. (1967). Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science, 12(5), 410–430.CrossRefGoogle Scholar
  42. Rödder, W., & Kulmann, F. (2006). Recall and reasoning—an information theoretical model of cognitive processes. Information Sciences, 176(17), 2439–2466.CrossRefGoogle Scholar
  43. Shamsfard, M., & Barforoush, A. A. (2004). Learning ontologies from natural language texts. International Journal of Human-Computer Studies, 60(1), 17–63.CrossRefGoogle Scholar
  44. Siemens, G. (2005a). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1),
  45. Siemens, G. (2005b). Connectivism: Learning as network-creation. Learning Circuits. November 2005,
  46. Slaughter, L. A., Soergel, D., & Rindflesch, T. C. (2006). Semantic representation of consumer questions and physician answers. International Journal of Medical Informatics, 75(7), 513–529.CrossRefGoogle Scholar
  47. SPSS Inc. (2008). SPSS text analysis for surveys (Version 3.0), Web:
  48. Storey, V. C., Dey, D., Ullrich, H., & Sundaresan, S. (1998). An ontology-based expert system for database design. Data & Knowledge Engineering, 28(1), 31–46.CrossRefGoogle Scholar
  49. Suereth, R. (1997). Developing natural language interfaces: Processing human conversations. New York: McGraw-Hill.Google Scholar
  50. Suppes, P., & Béziau, J. Y. (2004). Semantic computations of truth based on associations already learned. Journal of Applied Logic, 2(4), 457–467.CrossRefGoogle Scholar
  51. Tversky, A., & Kahneman, D. (2008). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. In J. E. Adler & L. J. Rips (Eds.), Reasoning: Studies of human inference and its foundations (pp. 114–135). Cambridge, New York: Cambridge University Press.Google Scholar
  52. Watts, D. J. (2003). Six degrees: The science of a connected age (1st ed.). New York: Norton.Google Scholar
  53. Yaner, P. W., & Goel, A. K. (2006). Visual analogy: Viewing analogical retrieval and mapping as constraint satisfaction problems. Applied Intelligence, 25(1), 91–105.CrossRefGoogle Scholar

Copyright information

© Springer Science & Business Media B.V. 2010

Authors and Affiliations

  • Kostas Alexandridis
    • 1
    • 2
  • Kristal Coe
    • 3
  • Stephen Garnett
    • 4
  1. 1.Center for Marine and Environmental Studies (CMES) and College of Science and MathematicsUniversity of the Virgin IslandsSt. ThomasUSA
  2. 2.CSIRO Sustainable EcosystemsDavies LaboratoryTownsvilleAustralia
  3. 3.Institute for Advanced StudiesCharles Darwin UniversityDarwinAustralia
  4. 4.School for Environmental Research, Institute for Advanced StudiesCharles Darwin UniversityDarwinAustralia

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