LIMITATIONS
Use of the internet as this method of collecting responses may pose a
hindrance to low-educated participants who may not know how to use the
internet or may have limited knowledge about utility (Alford et al.,
2010). Few studies grouped study demographics by highest level of
education completed jointly without stratification (Alford et al., 2010;
Kennedy et al., 2011; Lemke et al., 2010; Rodgers et al., 2018) which
may obscure results. Some studies were developed with outcome variables
that were hypothetical in nature in which researchers evaluated intent
or interest to participate in current or future genomic-related health
research in contrast with actual participation (Goldenberg et al., 2010;
McDonald et al., 2012; McDonald et al., 2012; Braunstein et al., 2008;
Henderson et al., 2008; Diaz et al., 2008). Previous studies have shown
that intention to participate does not convert to actual participation
(Donovan, 2000). Some study designs were cross-sectional which limits
the ability for researchers to analyze response rates over a period and
do not necessarily determine causal effects of participation barriers
(Cottler et al., 2013; Hurtado-de-Mendoza et al., 2016) while other
studies were exploratory or observational suggesting that research may
not have been well researched before and may represent response rates
that are not truthful or are skewed based on participants not being
situated in their natural setting when being observed (Ramirez et al.,
2015). All studies reporting participant socioeconomic demographics were
based on unreliable self-reporting and may potentially be skewed. Some
studies reported participation incentives (e.g., cash or gift) may
unduly encourage research participation and generate contrasting task
outcomes and unintended outcomes (Singer & Couper, 2008; Hsieh &
Kocielnik, 2016; Zutlevics, 2016). One study utilized language
interpreters to increase knowledge awareness among research personnel
(Gill et al., 2013). One study reported that data collection was
conducted prior to the passing of the Genetic Information
Nondiscrimination Act (GINA) of 2008 (Goldenberg et al., 2010) and this
may have influenced participant viewpoints. One study reported that no
adjustments were considered for categorical differences in disease areas
which may present challenges with disambiguating inferences drawn from
the data (Amiri et al., 2014). There was evidence in one study that
researchers made concerted efforts to demonstrate researcher
trustworthiness which may have influenced false-positive qualitative
data (Bussey-Jones et al., 2010). In another study, researchers
suggested recruitment biases (Bussey-Jones et al., 2010). One study
attested that due to the brevity of the data collection, results may be
untenable (Cottler et al., 2013). In another study, researchers
expressed that some participants may not have felt empowered to provide
input because other participants in the group were overly expressive or
outspoken in expressing their views (Lemke et al., 2010). Sanderson et
al., (2013) did not disclose to participants what genomic health
research was, including describing its significance to the viewpoint
assessment. In another study, researchers reported that responses to
viewpoint assessments may have possible generated findings from an
inherently biased sample (Bussey-Jones et al., 2010). Some studies
comprised participants who had already participated in participation
barrier studies which may have skewed response rates to the present
study (Bussey-Jones et al., 2010; Kapiriri et al., 2017). Sample size
was reported as small and not generalizable in many studies thus
manifesting low statistical power in approximating the priority
population (Spruill, Coleman, & Collins-McNeil, 2009; Sussner et al.,
2009; McDonald et al., 2012; McDonald et al., 2012; Jenkins et al.,
2009; Kennedy et al., 2011; Glenn, Chawla, & Bastani, 2012; Walker et
al., 2014; Hurtado-de-Mendoza et al., 2016; Ramirez et al., 2015; 2016;
Kapiriri et al., 2017).