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In the present study, owning a smartphone per se did not seem to increase the likelihood for using mental health interventions on such devices, given that the majority of participants appeared to own smartphones.
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In addition, no information was available about the likelihood for use of one biological agent over another.
A higher score on either measure reduced the likelihood of using condoms for females.
Studies have highlighted the likelihood of using herbs for the treatment of fever, especially among the elderly.
The Tamura-Nei plus gamma (TrN + G) model was selected as the optimal nucleotide substitution model for likelihood analyses using Modeltest with the AIC criterion [ 97, 98].
We ran RAxML 7.2.8 for maximum likelihood analyses using the WAG+CAMMA+F model for the whole data matrix.
We fit the likelihood for both methods using a Nelder-Mead maximization procedure and use 576 starting values in order to ensure that we reach the global maximum.
ProTest [ 114] was used to predict optimal evolutionary models for maximum likelihood analysis using Akaike Information Criteria (AIC) [ 115].
The software RAxML 7.2.8 [ 82, 83] was used for maximum likelihood analyses, using both the fast (−x) and the standard (−b) bootstrap algorithm with 200 replicates.
A random effects model was used for pooling of likelihood ratios using Review Manager 4.1 [ 66, 70, 72].
However, higher computer literacy in general (the frequent use of different technologies and devices) slightly increased the reported likelihood for the use of smartphones for mental health interventions.
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