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).