20 research outputs found
Additional file 2 of Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach
No full textAdditional file 2: Supplementary Table 1. Selected clinical conditions and related ICD-10 codes
Overview of respondents’ profile after data filtering (M = mean, SD = standard deviation, relative frequencies, n = number of respondents).
No full textOverview of respondents’ profile after data filtering (M = mean, SD = standard deviation, relative frequencies, n = number of respondents).</p
Relative frequencies pertaining to respondents’ engagement in secondary activities during manual driving (MD) and partially automated driving (PAD).
No full textRelative frequencies pertaining to respondents’ engagement in secondary activities during manual driving (MD) and partially automated driving (PAD).</p
Descriptive statistics of attitudinal questions (M, SD, 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree, n = number of respondents).
No full textMeans were ordered from highest to lowest in order to show high, moderate, and low mean ratings.</p
Measurement model.
No full textNote that the circles represent the latent (unobserved) constructs; arrows between the latent constructs represent the correlations / covariances between the latent constructs. The boxes represent the observed constructs (questionnaire items). Numbers on the arrows from the latent to the observed constructs represent the lambda’s (i.e., factor loadings). Small arrows underneath the boxes (observed constructs) represent the residuals (i.e., measurement error).</p
Additional file 1 of Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach
No full textAdditional file 1: Supplementary Figure 1. Selection of high-cost older adults
