Model of limitations - USA
By: Multiple Authors (see the references)
Epidemiologic evidence clearly shows
variations in incidence, prevalence of, and morbidity and mortality from
disease by population groups, yet we have had little success to eliminate these
differences (Anderson, 2012; Smedley, Stith, & Nelson, 2003). These
designations date from 1977, when the U.S. Census Bureau issued Directive 15
that continues to define five minimum racial/ethnic categories:
American Indian/Alaskan Native
Asian or Pacific Islander
Black
White
Hispanic (ethnic origin).
The Federal government has edited and
augmented these categories over time, even to the extent that people may
identify themselves with more than one race (e.g., Office of Management and
Budget (OMB), 1997; Jones & Bullock, 2012). Additionally, the U.S. Government
expanded its original (1977) designation of Hispanic ethnicity as “Hispanic or
Latino,” defining such “a person of Cuban, Mexican, Puerto Rican, South or
Central American, or other Spanish culture or origin, regardless of race” (OMB,
1997, p. 58789).
Since the 2000 U.S. Census, respondents have
been able to identify as belonging to multiple racial groups. According to the
2010 Census, one-third of people who reported multiple races also identified as
Latino/Hispanic; moreover, approximately half of people who identified as
Native Americans, Alaska Natives, Native Hawaiian and Other Pacific Islanders
also identified with more than one race (Jones & Bullock, 2012, p. 20). The
Federal government strives to account for these changes through its National
Standards for Culturally and Linguistically Appropriate Services (CLAS) in
Health and Health Care (Office of Minority Health, 2013).
Nevertheless, health scientists generally
continue to conduct research focused on the rational nature of humankind and
the belief of the underlying universality of the European American ways of
thinking and viewing reality (Hartigan, 2010; Henrich et al., 2010).
Changing demographics, particularly in the
United States, has brought cultural dissonance to the forefront in health care.
The sheer growth in the proportion of those of different social classes and
cultural backgrounds is changing the epidemiologic profiles of health as well
as the social structure of society.
Researchers, practitioners, and community
members represent this diversity and have begun to promote this approach to
research (Good, Willen, Hannah, Vickery, & Taeseng, 2011; Weisner, 2009).
Scientifically, finding intragroup variation is the current default
expectation, and distributional models of cultural beliefs and practices should
now be standard approaches. Accordingly, homogeneity would be the surprising
finding. Hence, assessing culture to learn about within as well as between
group variability of beliefs, values, practices and lived realities should also
be a standard practice wherever possible.
Peer-reviewed literature continues to publish
health research results that identify target populations by ethnic or language
groups and codes them as nominal variables. Yet anyone in the United States who
has telephoned for technical support and reached a native English speaker in
Bangalore, Dublin, or Manila recognizes the enduring irony in Dylan Thomas’
observation that similar native speakers are, “up against the barrier of a
common language” (1954, p. 146). Similarly, to propose a target population as
simply French, Portuguese, or Spanish-speaking can treat the group as
homogeneous in a target population’s beliefs and behaviors when a language
group may consist of multiple subgroups with varying health outcomes. It also assumes
that, with sufficient sample size, there is little or no measurement error
involved, i.e., that these ethnic or language groups, coded nominally, are
sufficient proxies for the hypothesized beliefs or behaviors thought to
characterize group differences. Such assumptions are both unrealistic and
untenable because they are unreliable in assessing and determining how cultural
norms affect health.
There lacks consensus on a definition of
culture and how to operationalize it in health research. Instead, the concept
of culture too often is used without any standardization among scientists and
practitioners alike. Given these disparate and occasionally over-simplistic
operationalizations, data collected on culture can be insufficient to account
for statistically significant results, or culture is rendered a residual
variable to account for the unexplained variance in health outcomes between diverse
groups. Such results likely contribute to the lack of success to bring equity
in health outcomes across multiple populations (Anderson, 2012; Smedley et al.,
2003). Regarding how to refer to different populations, we recognize the
growing preference for the term, populations of focus. Given the long-standing
use of target populations in health research, we use this latter term in this
multidisciplinary document.
Differences and disparities in health outcomes
by population groups have been well documented for 100 years (Smedley et al.,
2003), and behavioral sciences have recognized the limitations of our current
approaches to identify the causes of the differences. Efforts have been growing
to seek more refined ways to understand health behavior. One of the major
shifts in the last 15 years has been a more focused attention to measures of
race and ethnicity as variables in health research, but little attention has
focused on culture.
The simplistic modes of measurements currently
applied have rarely been questioned except by those trained in cross-culture
theory, and no concerted effort has been made to correct this bias. Therefore,
current health behavior research overlooks and misses the potential explanatory
power of culture. Measures or approaches that reduce culture to dichotomous or
nominal variables (e.g., African-American, non-Hispanic white, Japanese,
family-oriented or familismo, fatalism, Roman Catholic) erroneously assume
groups to be homogenous and static (Lakes, Lopez, & Garro, 2006; Schoenberg,
Drew, Stoller, & Kart, 2005).
Too often, these physical or philosophical
constructs are used as proxy cultural “markers” that are collected as data and
often only at intake, thus even hampering our ability to assess dynamic force
of these cultural constructs that inform beliefs, knowledge, norms, and
practices that influence behaviors at the individual, group, and institutional
levels of wellbeing, health, and care.
They also can contribute to the risk factors
known or suspected to impact disease prevalence, morbidity, and mortality in
diverse population groups (Dressler et al., 2005; Kagawa-Singer, 2006). Such
practice also results in the reproduction of stereotypes and over-generalized
representations of cultural practices or identities that have questionable
external validity and are of little use in either moving the science of health
behavior forward or improving equity in the health status of diverse
populations (Syme, 2008).
References:
Marjorie Kagawa-Singer, William W. Dressler, Sheba M.
George, William N. Elwood, with the assistance of a specially appointed expert panel,
2014, national institute of health NIH.
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