Fig 1.
A precision medicine implementation meta-theoretical framework.
Fig 2.
Hypothesized systems level precision medicine implementation mediation model.
Key: Large oval shapes = latent (unobservable) factors; Rectangles = indicator (observable) variables; Arrows = hypothesised correlation direction; Small circles = residual errors explaining measurements errors; Me = the mediator variable, a = the effect size of the independent variable on the mediator, b = the effect of the mediator on the dependent variable controlling for X, and c’ = the direct effect of X on Y controlling for Me.
Table 1.
Latent variable indicators.
Table 2.
Parameter estimates for fitted mediation model.
Fig 3.
Demographic summary of study participants.
Fig 4.
Quantile-Quantile (Q-Q) plot describing squared Mahalanobis distance (y-axis) against the quantiles of the chi-square distribution (x-axis).
Fig 5.
A. Survey responses to questionnaire section on characteristics of omics biomarkers. B. Survey responses on public genomic awareness presented as “user response”(UsR). C. Diverging stacked bar-charts for survey responses on implementation outcomes (ImO) construct.
Fig 6.
Structural model showing the relationship between omics-based biomarkers (OBM), public genomic awareness represented by user response (UsR) and precision medicine implementation outcomes (ImO).
Red lines indicate estimated parameters while green lines indicate fixed parameters.