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Logistic regression was used to model predictors of each unintentional non-adherence behavior.

A multivariate analysis of covariance model with backwards elimination was used to model predictors of beliefs about medicine.

We used multivariate logistic regression to model predictors of being a structured documenter, defined as using electronic templates or prepopulated dot phrases to document at least two of the three note sections (history, physical, assessment and plan).

The aim of this study was to model predictors of recall that could be used in clinical practise to help reduce the numbers of repeat examinations required due to blurring.

Those characteristics where there was a statistically significant (p<0.05) or borderline significant difference between people with low and adequate health literacy were entered into the multivariable model; logistic regression was used to model predictors of low health literacy.

The preference of log likelihood versus linear regression was again strongly significant (p < 0.0001) (Table  5).> After adjusting in the final two-level mixed-effects linear regression model, predictors of neonatal care practice existed both at the cluster level as well as at the individual level.

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The application of theoretical frameworks for modeling predictors of drug risk among male street laborers remains limited.

Finally, we modelled predictors of length of hospital stay for the majority of these terminally ill hospice patients who survived to discharge.

In logistic regression models, predictors of such failure included greater absolute value of spherical power (myopia/hyperopia), higher cylinder power (astigmatism), and not having worn spectacles at presentation.

Using a retrospective cohort study design, we modeled predictors of transfusion events within 24 hours of hospital admission and throughout the entire hospitalization.

The effects of model predictors were measured using odds ratios (95% CI) and P values.

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