Despite the entrenched acceptance of normal science in health care, it appears that authoritative, positivist, linear, risk averse, certainty-based thinking can only get us so far along the route of optimum health. This paper examines labor and childbirth as a paradigm case of a complex adaptive system (CAS) and offers the example of techniques used in a master-level course on normal childbirth to illustrate how maternity care clinicians can be introduced to complexity-based thinking through reflexive analysis of real life clinical (...) narratives. It suggests that these techniques might be used as a basis for answering the question, what is likely to work for this person, in this situation, given the range of evidence, and given their values and beliefs, my values and beliefs, and my clinical skills and knowledge? Topics discussed in this paper include complexity, pregnancy, and childbirth; mainstreaming complexity-focused decision making in maternity care; and using story in practice. In conclusion, complexity theory appears to offer a good explanatory model for pregnancy and childbirth, both in general, and for specific individuals. Future work in this area should examine the efficacy and limits to formal story telling in maternity care, with the aim of increasing optimal maternity care for mothers and babies, and for the staff and organizations that deliver the care. (PsycINFO Database Record (c) 2010 APA, all rights reserved). (shrink)

Purpose: The purpose of this study was to evaluate the veracity of a theoretically derived model of health that describes a non-linear trajectory of health from birth to death with available population data sets. Methods: The distribution of mortality by age is directly related to health at that age, thus health approximates 1/mortality. The inverse of available all-cause mortality data from various time periods and populations was used as proxy data to compare with the theoretically derived non-linear health model predictions, (...) using both qualitative approaches and quantitative one-sample Kolmogorov–Smirnov analysis with Monte Carlo simulation. Results: The mortality data's inverse resembles a log–normal distribution as predicted by the proposed health model. The curves have identical slopes from birth and follow a logarithmic decline from peak health in young adulthood. A majority of the sampled populations had a good to excellent quantitative fit to a log–normal distribution, supporting the underlying model assumptions. Post hoc manipulation showed the model predictions to be stable. Conclusions: This is a first theory of health to be validated by proxy data, namely the inverse of all-cause mortality. This non-linear model, derived from the notion of the interaction of physical, environmental, mental, emotional, social and sense-making domains of health, gives physicians a more rigorous basis to direct health care services and resources away from disease-focused elder care towards broad-based biopsychosocial interventions earlier in life. (shrink)