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LRM Social Science and Medicine Article.pdf


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78

L.R. Moore / Social Science & Medicine 105 (2014) 76e83

(Hacking, 1995, p.368). The interaction between category and
categorized, the feedback loop reshaping both person and category,
can continue ad infinitum.
One critical factor in disease looping is the addition of new
symptoms to the disease’s commonly understood illness prototype.
Bishop and Converse (1986, p.97) described illness prototypes as:
Stable representations. of the symptoms and other attributes
associated with particular disease entities. These disease prototypes are conceptual representations that serve as standards
against which to match and evaluate information about symptoms being experienced.
Illness prototypes are essential to the conceptualization of
illness and people’s perceptions of their bodies. People interpret
symptoms as problematic based on illness prototypes and ignore
symptoms without a corresponding prototype (Bishop & Converse,
1986; Kirmayer & Sartorius, 2007). Prototype changes, therefore,
must be examined in the face of rapid expansion of a diagnosis or
treatment category.
Like the looping effect, illness prototypes are largely created and
validated by medical professionals, and neither concept effectively
accounts for people who categorize themselves. Hacking (1995,
pp.381e2) acknowledges this limitation and predicts selfascriptive human kinds will “lead to a wholly new type of looping effect” as people “become the knowers, even if not the only
people authorized to have knowledge.”
Ecological niche and the proliferation of a diagnosis
The expansion of a category is not the only factor in the popularization of a diagnosis. To understand how a particular diagnosis
flourishes at a particular time, we can look to Hacking’s (1998) work
on the environmental niche and vectors of transient mental illness.
Contemplating the sudden rise of dissociative fugue in Europe in
the late 1800s, Hacking (1998, p.13) says,
We are struck by the phenomenon that some types of mental
illness and some arrangements of symptoms are central at some
times and places and absent in others.. I argue that one fruitful
idea for understanding transient mental illness is the ecological
niche, not just social, not just medical, not just coming from the
patient, not just from the doctors, but from the concatenation of
an extraordinarily large number of diverse types of elements
which for a moment provide a stable home for certain types of
manifestations of illness.
“Vectors” are these “diverse elements” that converge to form a
niche (Hacking, 1998, p.81). In the case of fugue, “medical taxonomy,
cultural polarity, observability, and release” coalesced to form a viable
fugue diagnosis. Hacking (1998, pp.81e2) uses the word vector “as a
metaphor.. [That] has the virtue of suggesting that different kinds of
phenomena, acting in different ways, but whose resultant may be a

possible niche in which mental illness may thrive.” The metaphor of
the ecological niche is particularly important because “it reminds us
that there must be many relevant vectors in play.”
A macro-level analysis of the many vectors contributing to the
proliferation of gluten free is beyond the scope of this article;
however, the interviews in this study illuminate how gluten free
has expanded through self-ascription on an individual level. Categorical expansion has facilitated the increasing appropriateness of a
gluten-free diet for many. Self-ascriptive looping can therefore be
understood as one of many vectors contributing to the proliferation
of the diet in the United States.
Methodology
These findings are based on 37 in-depth, semi-structured interviews conducted from May through October 2012 in Lawrence,
Kansas, a midwestern university town of 89,000 people. Services in
Lawrence draw dieters from hours away. Mainstream grocery
chains and two natural foods grocers carry extensive gluten-free
selections, a host of restaurants accommodate dietary restrictions,
and a well-known naturopathic practice is located in the town.
Ethical approval for the study was obtained through the Human
Subjects Committee at the University of Kansas in May 2012. Participants were located through convenience and snowball sampling
(Bernard, 2011, pp.147e9), including fliers in stores and restaurants,
word-of-mouth, classified ads, and at gluten-free events. One to
two hour interviews were conducted at a site of the participant’s
choosing, most frequently a café, and transcribed verbatim. Data
were analyzed in a two-step inductive coding process: open coding
followed by the application of focused codes based on four themes
from open coding (Emerson, Fretz, & Shaw, 2011): (1) negative
experience with a doctor, (2) undermines biomedicine, (3) unexpected relief of symptoms, and (4) diagnoses others.
Of the 37 participants, 31 had not received a formal diagnosis of
celiac disease. Those 31 interviews formed the primary data, while
participants with celiac disease provided contextual data. This study
has several limitations common to qualitative research. Sampling
was largely based on self-selection due to the dispersal of gluten-free
dieters. Additionally, the population of Lawrence is not itself representative of broader populations: the city has higher-than-average
educational attainment, and median household income is approximately $6000 lower than the national average (US Census, n.d.).
Despite these limitations, the interview data presented here show
how some individuals engage in the process of broadening the
diagnosis of a gluten-related disorder. Future studies should consider
larger and more diverse populations to expand on this research.
Results
Study participants
Tables 1e3 describe participants’ demographic characteristics.
Since non-celiac gluten-free dieters are the study’s focus, I provide

Table 1
On left: Gender, Median Age, and Median time gluten free of both celiac and non-celiac participants. On right: reasons for gluten-free diet among 31 non-celiac gluten free
participants.
Demographic information

Celiac

NCGF

Reason for diet among non-celiac gluten free

#

%

Female
Male
Median age
Median time gluten free

6
0
51.5 years
72 months

26
5
41 years
14 months

Self-diagnosed celiac disease
GRD diagnosed by alternative practitioner
Self-diagnosed GRD
GRD diagnosed by MD
Gluten free for other reasons: Weight loss,
Anxiety, Colitis, Autism, etc.

4
5
6
8
8

13%
16%
19%
26%
26%