Analysis
For each encounter, numerical values for each response were compared
among respondents (Table 1 and Figure 1). The degree of difference (DOD)
was calculated to capture concordance e.g. Question 3 states: “The
family understands the treatment options explained to them.” If the
family’s response was “Disagree,” a value of 4 was coded. If the
physician’s response was “Strongly Agree,” a value of 1 was coded, and
the DOD was three. A DOD ≤ one was considered concordance, while a DOD ≥
two was considered discordance (Table 2).
Each pair of responses for each of the first three questions was coded
as concordant or discordant. A repeated measures logistic regression
model was fit with terms for the subject, the pair of responders (e.g.,
family and provider, nurse and observer, etc.) and the question,
allowing a comparison of the rates of concordance between the questions,
and between the pairs of responders, while adjusting for the correlation
among responses.
Additionally, data from Questions 1-3 were reanalyzed using a
three-point scale where “Strongly Agree” and “Agree” was classified
as “Agree”; “Neutral” remained the same; and “Disagree” and
“Strongly Disagree” were classified as “Disagree”.
Finally, responses to open-ended questions were divided into 6 domains,
initially using the Bayer Institute for Health Care Communication E4
Model – Education, Empathy, Engagement, and
Enlistment.8 Each comment was analyzed by a blinded
scorer using these domains. Post-hoc analysis revealed several comments
addressing Speech Mechanics and Settings; these domains were added,
resulting in six domains for the analysis. (Figure 2)